# -*- coding: utf-8 -*-
# File: end2end_absa.py
# Time: 18:44 12/08/2025
# Author: YANG, HENG <hy345@exeter.ac.uk> (杨恒)
# Website: https://yangheng95.github.io
# GitHub: https://github.com/yangheng95
# HuggingFace: https://huggingface.co/yangheng
# Google Scholar: https://scholar.google.com/citations?user=NPq5a_0AAAAJ&hl=en
# Copyright (C) 2019-2025. All rights reserved.

# ====== New/Replacement imports ======
from typing import List, Dict, Any

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    AutoModelForTokenClassification,
    pipeline
)

import torch
import re
from pprint import pprint

# Device configuration
device = 0 if torch.cuda.is_available() else -1

# Model configurations
ASPECT_MODEL_ID = "yangheng/deberta-v3-base-end2end-absa"
SENTI_MODEL_ID = "yangheng/deberta-v3-base-absa-v1.1"

# When classifier's highest confidence < this threshold, optionally use extractor's ASP-XXX as fallback
USE_EXTRACTOR_SENTI_AS_FALLBACK = True
FALLBACK_CONFIDENCE_THRESHOLD = 0.8

# Maximum length and truncation strategy to avoid truncation warnings
TRUNCATION = True
MAX_LENGTH = 512  # Adjust based on GPU memory and text length
BATCH_SIZE = 16  # Batch size, adjust based on resources

# ================================

# Initialize tokenizer and model for aspect extraction
try:
    tok_asp = AutoTokenizer.from_pretrained(SENTI_MODEL_ID, use_fast=True)
    mdl_asp = AutoModelForTokenClassification.from_pretrained(ASPECT_MODEL_ID)

    aspect_extractor = pipeline(
        task="token-classification",
        model=mdl_asp,
        tokenizer=tok_asp,
        aggregation_strategy="simple",
        device=device,
    )
except Exception as e:
    print(f"Error loading aspect extraction models: {e}")
    raise

# Initialize sentiment classifier (text_pair mode)
try:
    sent_classifier = pipeline(
        task="text-classification",
        model=SENTI_MODEL_ID,
        device=device,
        return_all_scores=True,  # Return all class scores for confidence
        function_to_apply="softmax",
        top_k=None
    )
except Exception as e:
    print(f"Error loading sentiment classification model: {e}")
    raise


# --- Defensive fallback: when offset is missing, use substring search ---

def _clean_aspect(s: str) -> str:
    """Clean aspect string by removing leading/trailing punctuation and whitespace."""
    if not s:
        return ""
    return re.sub(r'^[\s\.,;:!?\(\)\[\]\{\}"\']+|[\s\.,;:!?\(\)\[\]\{\}"\']+$', "", s).strip()


def _locate_span(text: str, phrase: str):
    """Locate the span of a phrase in text, with case-insensitive fallback."""
    if not phrase:
        return None

    # Try exact match first
    pat = re.escape(phrase)
    m = re.search(pat, text)
    if m:
        return m.start(), m.end()

    # Try case-insensitive match
    m = re.search(pat, text, flags=re.IGNORECASE)
    if m:
        return m.start(), m.end()

    return None


def extract_aspects_for_text(text: str) -> List[Dict[str, Any]]:
    """
    Extract aspects from a single text using the aspect extraction model.

    Args:
        text: Input text to extract aspects from

    Returns:
        List of aspect dictionaries with position and metadata
    """
    if not text.strip():
        return []

    try:
        ents = aspect_extractor(text)
        print(f"Raw entities extracted: {ents}")  # Debug output
    except Exception as e:
        print(f"Error in aspect extraction for text '{text[:50]}...': {e}")
        return []

    aspects = []
    seen = set()

    for ent in ents:
        label = (ent.get("entity_group") or "").lower()
        print(f"Processing entity: {ent}, label: {label}")  # Debug output

        # More flexible label matching
        if not any(keyword in label for keyword in ["asp", "aspect", "b-", "i-"]):
            print(f"Skipping entity with label: {label}")
            continue

        word = _clean_aspect(ent.get("word", ""))
        if not word:  # Skip empty aspects
            print(f"Skipping empty aspect")
            continue

        start = ent.get("start")
        end = ent.get("end")

        # Fallback: when offset is missing, relocate using substring search
        if start is None or end is None:
            loc = _locate_span(text, word)
            if loc:
                start, end = loc
            else:
                # Skip if position cannot be determined to avoid dirty data
                print(f"Could not locate aspect '{word}' in text")
                continue

        # Avoid duplicates
        key = (word.lower(), int(start), int(end))
        if key in seen:
            continue
        seen.add(key)

        aspect_dict = {
            "aspect": word,
            "start": int(start),
            "end": int(end),
            "extractor_label": ent.get("entity_group", ""),
            "extractor_score": float(ent.get("score", 0.0)),
        }
        aspects.append(aspect_dict)
        print(f"Added aspect: {aspect_dict}")  # Debug output

    print(f"Final aspects for text: {aspects}")  # Debug output
    return aspects


def classify_aspects(text: str, aspects: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Classify sentiment polarity for each (text, aspect) pair.
    Since the aspect extractor already provides sentiment labels (ASP-Positive, etc.),
    we can use those directly or optionally run additional classification.

    Args:
        text: Original text
        aspects: List of aspect dictionaries

    Returns:
        List of aspects enriched with sentiment and confidence information
    """
    if not aspects:
        return []

    enriched = []
    for asp in aspects:
        # Extract sentiment from the extractor label (ASP-Positive -> Positive)
        extractor_label = asp.get("extractor_label", "")
        extractor_confidence = asp.get("extractor_score", 0.0)

        # Default values
        sentiment = "Neutral"
        confidence = extractor_confidence
        prob_map = {"Positive": 0.33, "Negative": 0.33, "Neutral": 0.33}

        # Parse sentiment from extractor label
        if "-" in extractor_label:
            _, maybe_sentiment = extractor_label.split("-", 1)
            if maybe_sentiment.capitalize() in ("Positive", "Negative", "Neutral"):
                sentiment = maybe_sentiment.capitalize()
                # Create probability distribution with extractor confidence
                prob_map = {
                    "Positive": confidence if sentiment == "Positive" else (1 - confidence) / 2,
                    "Negative": confidence if sentiment == "Negative" else (1 - confidence) / 2,
                    "Neutral": confidence if sentiment == "Neutral" else (1 - confidence) / 2
                }

        # Optionally run additional sentiment classification if confidence is low
        if confidence < FALLBACK_CONFIDENCE_THRESHOLD:
            try:
                # Try additional classification
                combined_input = f"{text} [SEP] {asp['aspect']}"

                result = sent_classifier(
                    combined_input,
                    truncation=TRUNCATION,
                    max_length=MAX_LENGTH
                )

                if result and isinstance(result, list) and len(result) > 0:
                    if isinstance(result[0], dict):
                        best = max(result, key=lambda d: d.get("score", 0))
                        classifier_sentiment = best.get("label", sentiment)
                        classifier_confidence = float(best.get("score", confidence))

                        # Use classifier result if it has higher confidence
                        if classifier_confidence > confidence:
                            sentiment = classifier_sentiment
                            confidence = classifier_confidence
                            prob_map = {d.get("label", "Neutral"): float(d.get("score", 0)) for d in result}

            except Exception as e:
                print(f"Error in additional classification for aspect '{asp['aspect']}': {e}")
                # Keep the extractor's result

        enriched.append({
            **asp,
            "sentiment": sentiment,
            "confidence": confidence,
            "probability": prob_map
        })

    return enriched


def absa(texts: List[str]) -> List[Dict[str, Any]]:
    """
    Main entry point: Extract aspects from each text and classify their sentiment polarity.

    Args:
        texts: List of input texts

    Returns:
        List of structured results for each text
    """
    if not texts:
        return []

    results = []
    for i, text in enumerate(texts):
        if not isinstance(text, str):
            print(f"Warning: Text at index {i} is not a string, skipping")
            continue

        try:
            aspects = extract_aspects_for_text(text)
            aspects = classify_aspects(text, aspects)

            results.append({
                "text": text,
                "aspects": [a["aspect"] for a in aspects],
                "positions": [[a["start"], a["end"]] for a in aspects],
                "sentiments": [a["sentiment"] for a in aspects],
                "confidence": [a["confidence"] for a in aspects],
                "details": aspects  # Preserve rich original information
            })
        except Exception as e:
            print(f"Error processing text at index {i}: {e}")
            results.append({
                "text": text,
                "aspects": [],
                "positions": [],
                "sentiments": [],
                "confidence": [],
                "details": []
            })

    return results


if __name__ == "__main__":
    # Test samples in multiple languages
    samples = [
        "The user interface is brilliant, but the documentation is a total mess.",
        # English
        "这家餐厅的牛排很好吃，但是服务很慢。",
        # Chinese (Simplified): The steak at this restaurant is delicious, but the service is slow.
        "La batería es malísima, aunque la cámara está muy bien.",
        # Spanish: The battery is terrible, although the camera is very good.
        "Le film était captivant, mais la fin était décevante.",
        # French: The movie was captivating, but the ending was disappointing.
        "Das Auto ist sehr sparsam, aber die Sitze sind unbequem.",
        # German: The car is very economical, but the seats are uncomfortable.
        "Il design è elegante, però il software è pieno di bug.",
        # Italian: The design is elegant, but the software is full of bugs.
        "O hotel tem uma vista incrível, mas o café da manhã é fraco.",
        # Portuguese: The hotel has an incredible view, but the breakfast is weak.
        "Книга очень интересная, но перевод оставляет желать лучшего.",
        # Russian: The book is very interesting, but the translation leaves much to be desired.
        "このアプリは便利だけど、バッテリーの消費が激しい。",
        # Japanese: This app is useful, but it drains the battery quickly.
        "음식은 맛있었지만, 가격이 너무 비쌌어요.",
        # Korean: The food was delicious, but the price was too expensive.
        "الخدمة ممتازة، لكن الموقع صعب الوصول إليه.",
        # Arabic: The service is excellent, but the location is hard to reach.
        "फ़ोन का कैमरा शानदार है, लेकिन बैटरी लाइफ खराब है।",
        # Hindi: The phone's camera is great, but the battery life is bad.
        "De locatie is perfect, alleen is het personeel onvriendelijk.",
        # Dutch: The location is perfect, however the staff is unfriendly.
        "Boken är välskriven, men handlingen är förutsägbar.",
        # Swedish: The book is well-written, but the plot is predictable.
        "Grafika w grze jest niesamowita, ale fabuła jest nudna.",
        # Polish: The graphics in the game are amazing, but the story is boring.
        "Ürün kaliteli görünüyor ama kargo çok geç geldi.",
        # Turkish: The product looks high quality, but the shipping was very late.
        "Chất lượng âm thanh tốt, tuy nhiên tai nghe không thoải mái lắm.",
        # Vietnamese: The sound quality is good, however the headphones are not very comfortable.
        "การแสดงดีมาก แต่บทภาพยนตร์ค่อนข้างอ่อน",
        # Thai: The acting was great, but the script was rather weak.
        "Η τοποθεσία είναι φανταστική, αλλά το δωμάτιο ήταν πολύ μικρό.",
        # Greek: The location is fantastic, but the room was very small.
        "המשחק מהנה, אבל יש בו יותר מדי פרסומות.",
        # Hebrew: The game is fun, but it has too many ads.
        "Ponsel ini cepat, tetapi cenderung cepat panas.",
        # Indonesian: This phone is fast, but it tends to get hot quickly.
        "Ohjelma on tehokas, mutta käyttöliittymä on sekava.",
        # Finnish: The program is powerful, but the user interface is confusing.
        "Maden var lækker, men portionerne var for små.",
        # Danish: The food was delicious, but the portions were too small.
        "Počítač je rychlý, ale software je zastaralý.",
        # Czech: The computer is fast, but the software is outdated.
    ]


    print("Running ABSA analysis...")
    try:
        results = absa(samples)
        print("\nResults:")
        pprint(results, width=120)
    except Exception as e:
        print(f"Error running ABSA: {e}")


# >>>> # Example output:
"""
Running ABSA analysis...
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.51754177), 'word': 'user interface', 'start': 3, 'end': 18}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.52044636), 'word': 'documentation', 'start': 40, 'end': 54}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.51754177), 'word': 'user interface', 'start': 3, 'end': 18}, label: asp-neutral
Added aspect: {'aspect': 'user interface', 'start': 3, 'end': 18, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.517541766166687}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.52044636), 'word': 'documentation', 'start': 40, 'end': 54}, label: asp-negative
Added aspect: {'aspect': 'documentation', 'start': 40, 'end': 54, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.5204463601112366}
Final aspects for text: [{'aspect': 'user interface', 'start': 3, 'end': 18, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.517541766166687}, {'aspect': 'documentation', 'start': 40, 'end': 54, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.5204463601112366}]
Raw entities extracted: [{'entity_group': 'ASP-Positive', 'score': np.float32(0.5346123), 'word': '牛排', 'start': 5, 'end': 7}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.5622819), 'word': '服务', 'start': 13, 'end': 15}]
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.5346123), 'word': '牛排', 'start': 5, 'end': 7}, label: asp-positive
Added aspect: {'aspect': '牛排', 'start': 5, 'end': 7, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.5346122980117798}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.5622819), 'word': '服务', 'start': 13, 'end': 15}, label: asp-negative
Added aspect: {'aspect': '服务', 'start': 13, 'end': 15, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.5622819066047668}
Final aspects for text: [{'aspect': '牛排', 'start': 5, 'end': 7, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.5346122980117798}, {'aspect': '服务', 'start': 13, 'end': 15, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.5622819066047668}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.4336498), 'word': 'batería', 'start': 2, 'end': 10}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.44364822), 'word': 'cámara', 'start': 33, 'end': 40}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.4336498), 'word': 'batería', 'start': 2, 'end': 10}, label: asp-neutral
Added aspect: {'aspect': 'batería', 'start': 2, 'end': 10, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.43364980816841125}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.44364822), 'word': 'cámara', 'start': 33, 'end': 40}, label: asp-neutral
Added aspect: {'aspect': 'cámara', 'start': 33, 'end': 40, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.44364821910858154}
Final aspects for text: [{'aspect': 'batería', 'start': 2, 'end': 10, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.43364980816841125}, {'aspect': 'cámara', 'start': 33, 'end': 40, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.44364821910858154}]
Raw entities extracted: [{'entity_group': 'ASP-Positive', 'score': np.float32(0.6539798), 'word': 'film', 'start': 2, 'end': 7}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.41829002), 'word': 'fin', 'start': 32, 'end': 36}]
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.6539798), 'word': 'film', 'start': 2, 'end': 7}, label: asp-positive
Added aspect: {'aspect': 'film', 'start': 2, 'end': 7, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.6539797782897949}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.41829002), 'word': 'fin', 'start': 32, 'end': 36}, label: asp-negative
Added aspect: {'aspect': 'fin', 'start': 32, 'end': 36, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.41829001903533936}
Final aspects for text: [{'aspect': 'film', 'start': 2, 'end': 7, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.6539797782897949}, {'aspect': 'fin', 'start': 32, 'end': 36, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.41829001903533936}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.5750168), 'word': 'Auto', 'start': 3, 'end': 8}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.49865413), 'word': 'Sitze', 'start': 35, 'end': 41}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5750168), 'word': 'Auto', 'start': 3, 'end': 8}, label: asp-neutral
Added aspect: {'aspect': 'Auto', 'start': 3, 'end': 8, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5750167965888977}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.49865413), 'word': 'Sitze', 'start': 35, 'end': 41}, label: asp-neutral
Added aspect: {'aspect': 'Sitze', 'start': 35, 'end': 41, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.4986541271209717}
Final aspects for text: [{'aspect': 'Auto', 'start': 3, 'end': 8, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5750167965888977}, {'aspect': 'Sitze', 'start': 35, 'end': 41, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.4986541271209717}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.6298937), 'word': 'design', 'start': 2, 'end': 9}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.605807), 'word': 'software', 'start': 29, 'end': 38}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.6298937), 'word': 'design', 'start': 2, 'end': 9}, label: asp-neutral
Added aspect: {'aspect': 'design', 'start': 2, 'end': 9, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.6298937201499939}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.605807), 'word': 'software', 'start': 29, 'end': 38}, label: asp-neutral
Added aspect: {'aspect': 'software', 'start': 29, 'end': 38, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.6058070063591003}
Final aspects for text: [{'aspect': 'design', 'start': 2, 'end': 9, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.6298937201499939}, {'aspect': 'software', 'start': 29, 'end': 38, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.6058070063591003}]
You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset
Raw entities extracted: [{'entity_group': 'ASP-Negative', 'score': np.float32(0.56500643), 'word': 'café', 'start': 37, 'end': 42}]
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.56500643), 'word': 'café', 'start': 37, 'end': 42}, label: asp-negative
Added aspect: {'aspect': 'café', 'start': 37, 'end': 42, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.56500643491745}
Final aspects for text: [{'aspect': 'café', 'start': 37, 'end': 42, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.56500643491745}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.42165104), 'word': 'Кни', 'start': 0, 'end': 3}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.56838894), 'word': 'пере', 'start': 26, 'end': 31}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.42165104), 'word': 'Кни', 'start': 0, 'end': 3}, label: asp-neutral
Added aspect: {'aspect': 'Кни', 'start': 0, 'end': 3, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.42165103554725647}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.56838894), 'word': 'пере', 'start': 26, 'end': 31}, label: asp-neutral
Added aspect: {'aspect': 'пере', 'start': 26, 'end': 31, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5683889389038086}
Final aspects for text: [{'aspect': 'Кни', 'start': 0, 'end': 3, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.42165103554725647}, {'aspect': 'пере', 'start': 26, 'end': 31, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5683889389038086}]
Raw entities extracted: [{'entity_group': 'ASP-Negative', 'score': np.float32(0.5808011), 'word': 'バッテリー', 'start': 12, 'end': 17}]
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.5808011), 'word': 'バッテリー', 'start': 12, 'end': 17}, label: asp-negative
Added aspect: {'aspect': 'バッテリー', 'start': 12, 'end': 17, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.5808011293411255}
Final aspects for text: [{'aspect': 'バッテリー', 'start': 12, 'end': 17, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.5808011293411255}]
Raw entities extracted: [{'entity_group': 'ASP-Positive', 'score': np.float32(0.49294925), 'word': '', 'start': 0, 'end': 1}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5432428), 'word': '음식', 'start': 0, 'end': 2}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.46728912), 'word': '가격', 'start': 10, 'end': 13}]
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.49294925), 'word': '', 'start': 0, 'end': 1}, label: asp-positive
Skipping empty aspect
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5432428), 'word': '음식', 'start': 0, 'end': 2}, label: asp-neutral
Added aspect: {'aspect': '음식', 'start': 0, 'end': 2, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5432428121566772}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.46728912), 'word': '가격', 'start': 10, 'end': 13}, label: asp-neutral
Added aspect: {'aspect': '가격', 'start': 10, 'end': 13, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.46728911995887756}
Final aspects for text: [{'aspect': '음식', 'start': 0, 'end': 2, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5432428121566772}, {'aspect': '가격', 'start': 10, 'end': 13, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.46728911995887756}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.5339548), 'word': 'الخ', 'start': 0, 'end': 3}, {'entity_group': 'ASP-Positive', 'score': np.float32(0.50554234), 'word': 'دمة', 'start': 3, 'end': 6}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.44124436), 'word': 'الموق', 'start': 18, 'end': 24}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.45630246), 'word': 'ع', 'start': 24, 'end': 25}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5339548), 'word': 'الخ', 'start': 0, 'end': 3}, label: asp-neutral
Added aspect: {'aspect': 'الخ', 'start': 0, 'end': 3, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5339547991752625}
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.50554234), 'word': 'دمة', 'start': 3, 'end': 6}, label: asp-positive
Added aspect: {'aspect': 'دمة', 'start': 3, 'end': 6, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.5055423378944397}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.44124436), 'word': 'الموق', 'start': 18, 'end': 24}, label: asp-neutral
Added aspect: {'aspect': 'الموق', 'start': 18, 'end': 24, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.44124436378479004}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.45630246), 'word': 'ع', 'start': 24, 'end': 25}, label: asp-negative
Added aspect: {'aspect': 'ع', 'start': 24, 'end': 25, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.4563024640083313}
Final aspects for text: [{'aspect': 'الخ', 'start': 0, 'end': 3, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5339547991752625}, {'aspect': 'دمة', 'start': 3, 'end': 6, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.5055423378944397}, {'aspect': 'الموق', 'start': 18, 'end': 24, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.44124436378479004}, {'aspect': 'ع', 'start': 24, 'end': 25, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.4563024640083313}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.45512593), 'word': 'कैमरा', 'start': 7, 'end': 13}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.47939613), 'word': 'बैट', 'start': 30, 'end': 34}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.43590102), 'word': 'री', 'start': 34, 'end': 36}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.46086523), 'word': 'लाइफ', 'start': 36, 'end': 41}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.45512593), 'word': 'कैमरा', 'start': 7, 'end': 13}, label: asp-neutral
Added aspect: {'aspect': 'कैमरा', 'start': 7, 'end': 13, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.45512592792510986}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.47939613), 'word': 'बैट', 'start': 30, 'end': 34}, label: asp-neutral
Added aspect: {'aspect': 'बैट', 'start': 30, 'end': 34, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.47939613461494446}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.43590102), 'word': 'री', 'start': 34, 'end': 36}, label: asp-negative
Added aspect: {'aspect': 'री', 'start': 34, 'end': 36, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.435901015996933}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.46086523), 'word': 'लाइफ', 'start': 36, 'end': 41}, label: asp-neutral
Added aspect: {'aspect': 'लाइफ', 'start': 36, 'end': 41, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.46086522936820984}
Final aspects for text: [{'aspect': 'कैमरा', 'start': 7, 'end': 13, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.45512592792510986}, {'aspect': 'बैट', 'start': 30, 'end': 34, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.47939613461494446}, {'aspect': 'री', 'start': 34, 'end': 36, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.435901015996933}, {'aspect': 'लाइफ', 'start': 36, 'end': 41, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.46086522936820984}]
Raw entities extracted: [{'entity_group': 'ASP-Positive', 'score': np.float32(0.4961163), 'word': 'locatie', 'start': 2, 'end': 10}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.48402262), 'word': 'personeel', 'start': 36, 'end': 46}]
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.4961163), 'word': 'locatie', 'start': 2, 'end': 10}, label: asp-positive
Added aspect: {'aspect': 'locatie', 'start': 2, 'end': 10, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.4961163103580475}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.48402262), 'word': 'personeel', 'start': 36, 'end': 46}, label: asp-negative
Added aspect: {'aspect': 'personeel', 'start': 36, 'end': 46, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.4840226173400879}
Final aspects for text: [{'aspect': 'locatie', 'start': 2, 'end': 10, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.4961163103580475}, {'aspect': 'personeel', 'start': 36, 'end': 46, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.4840226173400879}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.4699115), 'word': 'Bok', 'start': 0, 'end': 3}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.44758555), 'word': 'handling', 'start': 24, 'end': 33}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.4699115), 'word': 'Bok', 'start': 0, 'end': 3}, label: asp-neutral
Added aspect: {'aspect': 'Bok', 'start': 0, 'end': 3, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.46991148591041565}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.44758555), 'word': 'handling', 'start': 24, 'end': 33}, label: asp-negative
Added aspect: {'aspect': 'handling', 'start': 24, 'end': 33, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.4475855529308319}
Final aspects for text: [{'aspect': 'Bok', 'start': 0, 'end': 3, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.46991148591041565}, {'aspect': 'handling', 'start': 24, 'end': 33, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.4475855529308319}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.55233693), 'word': 'Grafika', 'start': 0, 'end': 7}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.38001376), 'word': 'g', 'start': 9, 'end': 11}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5456186), 'word': 'fabuła', 'start': 36, 'end': 43}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.55233693), 'word': 'Grafika', 'start': 0, 'end': 7}, label: asp-neutral
Added aspect: {'aspect': 'Grafika', 'start': 0, 'end': 7, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5523369312286377}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.38001376), 'word': 'g', 'start': 9, 'end': 11}, label: asp-neutral
Added aspect: {'aspect': 'g', 'start': 9, 'end': 11, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.38001376390457153}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5456186), 'word': 'fabuła', 'start': 36, 'end': 43}, label: asp-neutral
Added aspect: {'aspect': 'fabuła', 'start': 36, 'end': 43, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5456185936927795}
Final aspects for text: [{'aspect': 'Grafika', 'start': 0, 'end': 7, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5523369312286377}, {'aspect': 'g', 'start': 9, 'end': 11, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.38001376390457153}, {'aspect': 'fabuła', 'start': 36, 'end': 43, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5456185936927795}]
Raw entities extracted: [{'entity_group': 'ASP-Negative', 'score': np.float32(0.64053774), 'word': 'kargo', 'start': 27, 'end': 33}]
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.64053774), 'word': 'kargo', 'start': 27, 'end': 33}, label: asp-negative
Added aspect: {'aspect': 'kargo', 'start': 27, 'end': 33, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.6405377388000488}
Final aspects for text: [{'aspect': 'kargo', 'start': 27, 'end': 33, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.6405377388000488}]
Raw entities extracted: [{'entity_group': 'ASP-Positive', 'score': np.float32(0.6112579), 'word': 'Ch', 'start': 0, 'end': 2}, {'entity_group': 'ASP-Positive', 'score': np.float32(0.6159069), 'word': 'lượng âm thanh', 'start': 4, 'end': 19}]
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.6112579), 'word': 'Ch', 'start': 0, 'end': 2}, label: asp-positive
Added aspect: {'aspect': 'Ch', 'start': 0, 'end': 2, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.6112579107284546}
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.6159069), 'word': 'lượng âm thanh', 'start': 4, 'end': 19}, label: asp-positive
Added aspect: {'aspect': 'lượng âm thanh', 'start': 4, 'end': 19, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.6159068942070007}
Final aspects for text: [{'aspect': 'Ch', 'start': 0, 'end': 2, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.6112579107284546}, {'aspect': 'lượng âm thanh', 'start': 4, 'end': 19, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.6159068942070007}]
Raw entities extracted: []
Final aspects for text: []
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.64732456), 'word': 'τοποθεσία', 'start': 1, 'end': 11}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.49225593), 'word': 'δω', 'start': 37, 'end': 40}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.4857553), 'word': 'μάτιο', 'start': 40, 'end': 45}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.64732456), 'word': 'τοποθεσία', 'start': 1, 'end': 11}, label: asp-neutral
Added aspect: {'aspect': 'τοποθεσία', 'start': 1, 'end': 11, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.6473245620727539}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.49225593), 'word': 'δω', 'start': 37, 'end': 40}, label: asp-negative
Added aspect: {'aspect': 'δω', 'start': 37, 'end': 40, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.49225592613220215}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.4857553), 'word': 'μάτιο', 'start': 40, 'end': 45}, label: asp-neutral
Added aspect: {'aspect': 'μάτιο', 'start': 40, 'end': 45, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.4857552945613861}
Final aspects for text: [{'aspect': 'τοποθεσία', 'start': 1, 'end': 11, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.6473245620727539}, {'aspect': 'δω', 'start': 37, 'end': 40, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.49225592613220215}, {'aspect': 'μάτιο', 'start': 40, 'end': 45, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.4857552945613861}]
Raw entities extracted: [{'entity_group': 'ASP-Positive', 'score': np.float32(0.41735768), 'word': 'ה', 'start': 0, 'end': 1}, {'entity_group': 'ASP-Positive', 'score': np.float32(0.39395496), 'word': 'משחק', 'start': 1, 'end': 5}]
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.41735768), 'word': 'ה', 'start': 0, 'end': 1}, label: asp-positive
Added aspect: {'aspect': 'ה', 'start': 0, 'end': 1, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.4173576831817627}
Processing entity: {'entity_group': 'ASP-Positive', 'score': np.float32(0.39395496), 'word': 'משחק', 'start': 1, 'end': 5}, label: asp-positive
Added aspect: {'aspect': 'משחק', 'start': 1, 'end': 5, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.39395496249198914}
Final aspects for text: [{'aspect': 'ה', 'start': 0, 'end': 1, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.4173576831817627}, {'aspect': 'משחק', 'start': 1, 'end': 5, 'extractor_label': 'ASP-Positive', 'extractor_score': 0.39395496249198914}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.5927124), 'word': 'Pons', 'start': 0, 'end': 4}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.281419), 'word': 'el', 'start': 4, 'end': 6}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.56265074), 'word': 'tetapi', 'start': 17, 'end': 24}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5927124), 'word': 'Pons', 'start': 0, 'end': 4}, label: asp-neutral
Added aspect: {'aspect': 'Pons', 'start': 0, 'end': 4, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.59271240234375}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.281419), 'word': 'el', 'start': 4, 'end': 6}, label: asp-neutral
Added aspect: {'aspect': 'el', 'start': 4, 'end': 6, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.2814190089702606}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.56265074), 'word': 'tetapi', 'start': 17, 'end': 24}, label: asp-neutral
Added aspect: {'aspect': 'tetapi', 'start': 17, 'end': 24, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.562650740146637}
Final aspects for text: [{'aspect': 'Pons', 'start': 0, 'end': 4, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.59271240234375}, {'aspect': 'el', 'start': 4, 'end': 6, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.2814190089702606}, {'aspect': 'tetapi', 'start': 17, 'end': 24, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.562650740146637}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.50318885), 'word': 'Ohjelma', 'start': 0, 'end': 7}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.48775986), 'word': 'käyttöliittymä', 'start': 25, 'end': 40}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.50318885), 'word': 'Ohjelma', 'start': 0, 'end': 7}, label: asp-neutral
Added aspect: {'aspect': 'Ohjelma', 'start': 0, 'end': 7, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5031888484954834}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.48775986), 'word': 'käyttöliittymä', 'start': 25, 'end': 40}, label: asp-neutral
Added aspect: {'aspect': 'käyttöliittymä', 'start': 25, 'end': 40, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.48775985836982727}
Final aspects for text: [{'aspect': 'Ohjelma', 'start': 0, 'end': 7, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5031888484954834}, {'aspect': 'käyttöliittymä', 'start': 25, 'end': 40, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.48775985836982727}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.4489361), 'word': 'Maden', 'start': 0, 'end': 5}, {'entity_group': 'ASP-Negative', 'score': np.float32(0.5446483), 'word': 'portion', 'start': 21, 'end': 29}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.4489361), 'word': 'Maden', 'start': 0, 'end': 5}, label: asp-neutral
Added aspect: {'aspect': 'Maden', 'start': 0, 'end': 5, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.4489361047744751}
Processing entity: {'entity_group': 'ASP-Negative', 'score': np.float32(0.5446483), 'word': 'portion', 'start': 21, 'end': 29}, label: asp-negative
Added aspect: {'aspect': 'portion', 'start': 21, 'end': 29, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.544648289680481}
Final aspects for text: [{'aspect': 'Maden', 'start': 0, 'end': 5, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.4489361047744751}, {'aspect': 'portion', 'start': 21, 'end': 29, 'extractor_label': 'ASP-Negative', 'extractor_score': 0.544648289680481}]
Raw entities extracted: [{'entity_group': 'ASP-Neutral', 'score': np.float32(0.5072303), 'word': 'Počítač', 'start': 0, 'end': 7}, {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5066106), 'word': 'software', 'start': 22, 'end': 31}]
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5072303), 'word': 'Počítač', 'start': 0, 'end': 7}, label: asp-neutral
Added aspect: {'aspect': 'Počítač', 'start': 0, 'end': 7, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.507230281829834}
Processing entity: {'entity_group': 'ASP-Neutral', 'score': np.float32(0.5066106), 'word': 'software', 'start': 22, 'end': 31}, label: asp-neutral
Added aspect: {'aspect': 'software', 'start': 22, 'end': 31, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5066105723381042}
Final aspects for text: [{'aspect': 'Počítač', 'start': 0, 'end': 7, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.507230281829834}, {'aspect': 'software', 'start': 22, 'end': 31, 'extractor_label': 'ASP-Neutral', 'extractor_score': 0.5066105723381042}]

Results:
[{'aspects': ['user interface', 'documentation'],
  'confidence': [0.517541766166687, 0.5204463601112366],
  'details': [{'aspect': 'user interface',
               'confidence': 0.517541766166687,
               'end': 18,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.517541766166687,
               'probability': {'Negative': 0.2412291169166565,
                               'Neutral': 0.517541766166687,
                               'Positive': 0.2412291169166565},
               'sentiment': 'Neutral',
               'start': 3},
              {'aspect': 'documentation',
               'confidence': 0.5204463601112366,
               'end': 54,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.5204463601112366,
               'probability': {'Negative': 0.5204463601112366,
                               'Neutral': 0.2397768199443817,
                               'Positive': 0.2397768199443817},
               'sentiment': 'Negative',
               'start': 40}],
  'positions': [[3, 18], [40, 54]],
  'sentiments': ['Neutral', 'Negative'],
  'text': 'The user interface is brilliant, but the documentation is a total mess.'},
 {'aspects': ['牛排', '服务'],
  'confidence': [0.5346122980117798, 0.5622819066047668],
  'details': [{'aspect': '牛排',
               'confidence': 0.5346122980117798,
               'end': 7,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.5346122980117798,
               'probability': {'Negative': 0.2326938509941101,
                               'Neutral': 0.2326938509941101,
                               'Positive': 0.5346122980117798},
               'sentiment': 'Positive',
               'start': 5},
              {'aspect': '服务',
               'confidence': 0.5622819066047668,
               'end': 15,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.5622819066047668,
               'probability': {'Negative': 0.5622819066047668,
                               'Neutral': 0.21885904669761658,
                               'Positive': 0.21885904669761658},
               'sentiment': 'Negative',
               'start': 13}],
  'positions': [[5, 7], [13, 15]],
  'sentiments': ['Positive', 'Negative'],
  'text': '这家餐厅的牛排很好吃，但是服务很慢。'},
 {'aspects': ['batería', 'cámara'],
  'confidence': [0.43364980816841125, 0.44364821910858154],
  'details': [{'aspect': 'batería',
               'confidence': 0.43364980816841125,
               'end': 10,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.43364980816841125,
               'probability': {'Negative': 0.2831750959157944,
                               'Neutral': 0.43364980816841125,
                               'Positive': 0.2831750959157944},
               'sentiment': 'Neutral',
               'start': 2},
              {'aspect': 'cámara',
               'confidence': 0.44364821910858154,
               'end': 40,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.44364821910858154,
               'probability': {'Negative': 0.27817589044570923,
                               'Neutral': 0.44364821910858154,
                               'Positive': 0.27817589044570923},
               'sentiment': 'Neutral',
               'start': 33}],
  'positions': [[2, 10], [33, 40]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': 'La batería es malísima, aunque la cámara está muy bien.'},
 {'aspects': ['film', 'fin'],
  'confidence': [0.6539797782897949, 0.41829001903533936],
  'details': [{'aspect': 'film',
               'confidence': 0.6539797782897949,
               'end': 7,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.6539797782897949,
               'probability': {'Negative': 0.17301011085510254,
                               'Neutral': 0.17301011085510254,
                               'Positive': 0.6539797782897949},
               'sentiment': 'Positive',
               'start': 2},
              {'aspect': 'fin',
               'confidence': 0.41829001903533936,
               'end': 36,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.41829001903533936,
               'probability': {'Negative': 0.41829001903533936,
                               'Neutral': 0.2908549904823303,
                               'Positive': 0.2908549904823303},
               'sentiment': 'Negative',
               'start': 32}],
  'positions': [[2, 7], [32, 36]],
  'sentiments': ['Positive', 'Negative'],
  'text': 'Le film était captivant, mais la fin était décevante.'},
 {'aspects': ['Auto', 'Sitze'],
  'confidence': [0.5750167965888977, 0.4986541271209717],
  'details': [{'aspect': 'Auto',
               'confidence': 0.5750167965888977,
               'end': 8,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5750167965888977,
               'probability': {'Negative': 0.21249160170555115,
                               'Neutral': 0.5750167965888977,
                               'Positive': 0.21249160170555115},
               'sentiment': 'Neutral',
               'start': 3},
              {'aspect': 'Sitze',
               'confidence': 0.4986541271209717,
               'end': 41,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.4986541271209717,
               'probability': {'Negative': 0.25067293643951416,
                               'Neutral': 0.4986541271209717,
                               'Positive': 0.25067293643951416},
               'sentiment': 'Neutral',
               'start': 35}],
  'positions': [[3, 8], [35, 41]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': 'Das Auto ist sehr sparsam, aber die Sitze sind unbequem.'},
 {'aspects': ['design', 'software'],
  'confidence': [0.6298937201499939, 0.6058070063591003],
  'details': [{'aspect': 'design',
               'confidence': 0.6298937201499939,
               'end': 9,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.6298937201499939,
               'probability': {'Negative': 0.18505313992500305,
                               'Neutral': 0.6298937201499939,
                               'Positive': 0.18505313992500305},
               'sentiment': 'Neutral',
               'start': 2},
              {'aspect': 'software',
               'confidence': 0.6058070063591003,
               'end': 38,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.6058070063591003,
               'probability': {'Negative': 0.19709649682044983,
                               'Neutral': 0.6058070063591003,
                               'Positive': 0.19709649682044983},
               'sentiment': 'Neutral',
               'start': 29}],
  'positions': [[2, 9], [29, 38]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': 'Il design è elegante, però il software è pieno di bug.'},
 {'aspects': ['café'],
  'confidence': [0.56500643491745],
  'details': [{'aspect': 'café',
               'confidence': 0.56500643491745,
               'end': 42,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.56500643491745,
               'probability': {'Negative': 0.56500643491745,
                               'Neutral': 0.21749678254127502,
                               'Positive': 0.21749678254127502},
               'sentiment': 'Negative',
               'start': 37}],
  'positions': [[37, 42]],
  'sentiments': ['Negative'],
  'text': 'O hotel tem uma vista incrível, mas o café da manhã é fraco.'},
 {'aspects': ['Кни', 'пере'],
  'confidence': [0.42165103554725647, 0.5683889389038086],
  'details': [{'aspect': 'Кни',
               'confidence': 0.42165103554725647,
               'end': 3,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.42165103554725647,
               'probability': {'Negative': 0.28917448222637177,
                               'Neutral': 0.42165103554725647,
                               'Positive': 0.28917448222637177},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'пере',
               'confidence': 0.5683889389038086,
               'end': 31,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5683889389038086,
               'probability': {'Negative': 0.2158055305480957,
                               'Neutral': 0.5683889389038086,
                               'Positive': 0.2158055305480957},
               'sentiment': 'Neutral',
               'start': 26}],
  'positions': [[0, 3], [26, 31]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': 'Книга очень интересная, но перевод оставляет желать лучшего.'},
 {'aspects': ['バッテリー'],
  'confidence': [0.5808011293411255],
  'details': [{'aspect': 'バッテリー',
               'confidence': 0.5808011293411255,
               'end': 17,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.5808011293411255,
               'probability': {'Negative': 0.5808011293411255,
                               'Neutral': 0.20959943532943726,
                               'Positive': 0.20959943532943726},
               'sentiment': 'Negative',
               'start': 12}],
  'positions': [[12, 17]],
  'sentiments': ['Negative'],
  'text': 'このアプリは便利だけど、バッテリーの消費が激しい。'},
 {'aspects': ['음식', '가격'],
  'confidence': [0.5432428121566772, 0.46728911995887756],
  'details': [{'aspect': '음식',
               'confidence': 0.5432428121566772,
               'end': 2,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5432428121566772,
               'probability': {'Negative': 0.22837859392166138,
                               'Neutral': 0.5432428121566772,
                               'Positive': 0.22837859392166138},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': '가격',
               'confidence': 0.46728911995887756,
               'end': 13,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.46728911995887756,
               'probability': {'Negative': 0.2663554400205612,
                               'Neutral': 0.46728911995887756,
                               'Positive': 0.2663554400205612},
               'sentiment': 'Neutral',
               'start': 10}],
  'positions': [[0, 2], [10, 13]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': '음식은 맛있었지만, 가격이 너무 비쌌어요.'},
 {'aspects': ['الخ', 'دمة', 'الموق', 'ع'],
  'confidence': [0.5339547991752625, 0.5055423378944397, 0.44124436378479004, 0.4563024640083313],
  'details': [{'aspect': 'الخ',
               'confidence': 0.5339547991752625,
               'end': 3,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5339547991752625,
               'probability': {'Negative': 0.23302260041236877,
                               'Neutral': 0.5339547991752625,
                               'Positive': 0.23302260041236877},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'دمة',
               'confidence': 0.5055423378944397,
               'end': 6,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.5055423378944397,
               'probability': {'Negative': 0.24722883105278015,
                               'Neutral': 0.24722883105278015,
                               'Positive': 0.5055423378944397},
               'sentiment': 'Positive',
               'start': 3},
              {'aspect': 'الموق',
               'confidence': 0.44124436378479004,
               'end': 24,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.44124436378479004,
               'probability': {'Negative': 0.279377818107605,
                               'Neutral': 0.44124436378479004,
                               'Positive': 0.279377818107605},
               'sentiment': 'Neutral',
               'start': 18},
              {'aspect': 'ع',
               'confidence': 0.4563024640083313,
               'end': 25,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.4563024640083313,
               'probability': {'Negative': 0.4563024640083313,
                               'Neutral': 0.27184876799583435,
                               'Positive': 0.27184876799583435},
               'sentiment': 'Negative',
               'start': 24}],
  'positions': [[0, 3], [3, 6], [18, 24], [24, 25]],
  'sentiments': ['Neutral', 'Positive', 'Neutral', 'Negative'],
  'text': 'الخدمة ممتازة، لكن الموقع صعب الوصول إليه.'},
 {'aspects': ['कैमरा', 'बैट', 'री', 'लाइफ'],
  'confidence': [0.45512592792510986, 0.47939613461494446, 0.435901015996933, 0.46086522936820984],
  'details': [{'aspect': 'कैमरा',
               'confidence': 0.45512592792510986,
               'end': 13,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.45512592792510986,
               'probability': {'Negative': 0.27243703603744507,
                               'Neutral': 0.45512592792510986,
                               'Positive': 0.27243703603744507},
               'sentiment': 'Neutral',
               'start': 7},
              {'aspect': 'बैट',
               'confidence': 0.47939613461494446,
               'end': 34,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.47939613461494446,
               'probability': {'Negative': 0.26030193269252777,
                               'Neutral': 0.47939613461494446,
                               'Positive': 0.26030193269252777},
               'sentiment': 'Neutral',
               'start': 30},
              {'aspect': 'री',
               'confidence': 0.435901015996933,
               'end': 36,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.435901015996933,
               'probability': {'Negative': 0.435901015996933,
                               'Neutral': 0.2820494920015335,
                               'Positive': 0.2820494920015335},
               'sentiment': 'Negative',
               'start': 34},
              {'aspect': 'लाइफ',
               'confidence': 0.46086522936820984,
               'end': 41,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.46086522936820984,
               'probability': {'Negative': 0.2695673853158951,
                               'Neutral': 0.46086522936820984,
                               'Positive': 0.2695673853158951},
               'sentiment': 'Neutral',
               'start': 36}],
  'positions': [[7, 13], [30, 34], [34, 36], [36, 41]],
  'sentiments': ['Neutral', 'Neutral', 'Negative', 'Neutral'],
  'text': 'फ़ोन का कैमरा शानदार है, लेकिन बैटरी लाइफ खराब है।'},
 {'aspects': ['locatie', 'personeel'],
  'confidence': [0.4961163103580475, 0.4840226173400879],
  'details': [{'aspect': 'locatie',
               'confidence': 0.4961163103580475,
               'end': 10,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.4961163103580475,
               'probability': {'Negative': 0.25194184482097626,
                               'Neutral': 0.25194184482097626,
                               'Positive': 0.4961163103580475},
               'sentiment': 'Positive',
               'start': 2},
              {'aspect': 'personeel',
               'confidence': 0.4840226173400879,
               'end': 46,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.4840226173400879,
               'probability': {'Negative': 0.4840226173400879,
                               'Neutral': 0.25798869132995605,
                               'Positive': 0.25798869132995605},
               'sentiment': 'Negative',
               'start': 36}],
  'positions': [[2, 10], [36, 46]],
  'sentiments': ['Positive', 'Negative'],
  'text': 'De locatie is perfect, alleen is het personeel onvriendelijk.'},
 {'aspects': ['Bok', 'handling'],
  'confidence': [0.46991148591041565, 0.4475855529308319],
  'details': [{'aspect': 'Bok',
               'confidence': 0.46991148591041565,
               'end': 3,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.46991148591041565,
               'probability': {'Negative': 0.2650442570447922,
                               'Neutral': 0.46991148591041565,
                               'Positive': 0.2650442570447922},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'handling',
               'confidence': 0.4475855529308319,
               'end': 33,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.4475855529308319,
               'probability': {'Negative': 0.4475855529308319,
                               'Neutral': 0.27620722353458405,
                               'Positive': 0.27620722353458405},
               'sentiment': 'Negative',
               'start': 24}],
  'positions': [[0, 3], [24, 33]],
  'sentiments': ['Neutral', 'Negative'],
  'text': 'Boken är välskriven, men handlingen är förutsägbar.'},
 {'aspects': ['Grafika', 'g', 'fabuła'],
  'confidence': [0.5523369312286377, 0.38001376390457153, 0.5456185936927795],
  'details': [{'aspect': 'Grafika',
               'confidence': 0.5523369312286377,
               'end': 7,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5523369312286377,
               'probability': {'Negative': 0.22383153438568115,
                               'Neutral': 0.5523369312286377,
                               'Positive': 0.22383153438568115},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'g',
               'confidence': 0.38001376390457153,
               'end': 11,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.38001376390457153,
               'probability': {'Negative': 0.30999311804771423,
                               'Neutral': 0.38001376390457153,
                               'Positive': 0.30999311804771423},
               'sentiment': 'Neutral',
               'start': 9},
              {'aspect': 'fabuła',
               'confidence': 0.5456185936927795,
               'end': 43,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5456185936927795,
               'probability': {'Negative': 0.22719070315361023,
                               'Neutral': 0.5456185936927795,
                               'Positive': 0.22719070315361023},
               'sentiment': 'Neutral',
               'start': 36}],
  'positions': [[0, 7], [9, 11], [36, 43]],
  'sentiments': ['Neutral', 'Neutral', 'Neutral'],
  'text': 'Grafika w grze jest niesamowita, ale fabuła jest nudna.'},
 {'aspects': ['kargo'],
  'confidence': [0.6405377388000488],
  'details': [{'aspect': 'kargo',
               'confidence': 0.6405377388000488,
               'end': 33,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.6405377388000488,
               'probability': {'Negative': 0.6405377388000488,
                               'Neutral': 0.17973113059997559,
                               'Positive': 0.17973113059997559},
               'sentiment': 'Negative',
               'start': 27}],
  'positions': [[27, 33]],
  'sentiments': ['Negative'],
  'text': 'Ürün kaliteli görünüyor ama kargo çok geç geldi.'},
 {'aspects': ['Ch', 'lượng âm thanh'],
  'confidence': [0.6112579107284546, 0.6159068942070007],
  'details': [{'aspect': 'Ch',
               'confidence': 0.6112579107284546,
               'end': 2,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.6112579107284546,
               'probability': {'Negative': 0.1943710446357727,
                               'Neutral': 0.1943710446357727,
                               'Positive': 0.6112579107284546},
               'sentiment': 'Positive',
               'start': 0},
              {'aspect': 'lượng âm thanh',
               'confidence': 0.6159068942070007,
               'end': 19,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.6159068942070007,
               'probability': {'Negative': 0.19204655289649963,
                               'Neutral': 0.19204655289649963,
                               'Positive': 0.6159068942070007},
               'sentiment': 'Positive',
               'start': 4}],
  'positions': [[0, 2], [4, 19]],
  'sentiments': ['Positive', 'Positive'],
  'text': 'Chất lượng âm thanh tốt, tuy nhiên tai nghe không thoải mái lắm.'},
 {'aspects': [],
  'confidence': [],
  'details': [],
  'positions': [],
  'sentiments': [],
  'text': 'การแสดงดีมาก แต่บทภาพยนตร์ค่อนข้างอ่อน'},
 {'aspects': ['τοποθεσία', 'δω', 'μάτιο'],
  'confidence': [0.6473245620727539, 0.49225592613220215, 0.4857552945613861],
  'details': [{'aspect': 'τοποθεσία',
               'confidence': 0.6473245620727539,
               'end': 11,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.6473245620727539,
               'probability': {'Negative': 0.17633771896362305,
                               'Neutral': 0.6473245620727539,
                               'Positive': 0.17633771896362305},
               'sentiment': 'Neutral',
               'start': 1},
              {'aspect': 'δω',
               'confidence': 0.49225592613220215,
               'end': 40,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.49225592613220215,
               'probability': {'Negative': 0.49225592613220215,
                               'Neutral': 0.2538720369338989,
                               'Positive': 0.2538720369338989},
               'sentiment': 'Negative',
               'start': 37},
              {'aspect': 'μάτιο',
               'confidence': 0.4857552945613861,
               'end': 45,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.4857552945613861,
               'probability': {'Negative': 0.25712235271930695,
                               'Neutral': 0.4857552945613861,
                               'Positive': 0.25712235271930695},
               'sentiment': 'Neutral',
               'start': 40}],
  'positions': [[1, 11], [37, 40], [40, 45]],
  'sentiments': ['Neutral', 'Negative', 'Neutral'],
  'text': 'Η τοποθεσία είναι φανταστική, αλλά το δωμάτιο ήταν πολύ μικρό.'},
 {'aspects': ['ה', 'משחק'],
  'confidence': [0.4173576831817627, 0.39395496249198914],
  'details': [{'aspect': 'ה',
               'confidence': 0.4173576831817627,
               'end': 1,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.4173576831817627,
               'probability': {'Negative': 0.29132115840911865,
                               'Neutral': 0.29132115840911865,
                               'Positive': 0.4173576831817627},
               'sentiment': 'Positive',
               'start': 0},
              {'aspect': 'משחק',
               'confidence': 0.39395496249198914,
               'end': 5,
               'extractor_label': 'ASP-Positive',
               'extractor_score': 0.39395496249198914,
               'probability': {'Negative': 0.30302251875400543,
                               'Neutral': 0.30302251875400543,
                               'Positive': 0.39395496249198914},
               'sentiment': 'Positive',
               'start': 1}],
  'positions': [[0, 1], [1, 5]],
  'sentiments': ['Positive', 'Positive'],
  'text': 'המשחק מהנה, אבל יש בו יותר מדי פרסומות.'},
 {'aspects': ['Pons', 'el', 'tetapi'],
  'confidence': [0.59271240234375, 0.2814190089702606, 0.562650740146637],
  'details': [{'aspect': 'Pons',
               'confidence': 0.59271240234375,
               'end': 4,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.59271240234375,
               'probability': {'Negative': 0.203643798828125,
                               'Neutral': 0.59271240234375,
                               'Positive': 0.203643798828125},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'el',
               'confidence': 0.2814190089702606,
               'end': 6,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.2814190089702606,
               'probability': {'Negative': 0.3592904955148697,
                               'Neutral': 0.2814190089702606,
                               'Positive': 0.3592904955148697},
               'sentiment': 'Neutral',
               'start': 4},
              {'aspect': 'tetapi',
               'confidence': 0.562650740146637,
               'end': 24,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.562650740146637,
               'probability': {'Negative': 0.21867462992668152,
                               'Neutral': 0.562650740146637,
                               'Positive': 0.21867462992668152},
               'sentiment': 'Neutral',
               'start': 17}],
  'positions': [[0, 4], [4, 6], [17, 24]],
  'sentiments': ['Neutral', 'Neutral', 'Neutral'],
  'text': 'Ponsel ini cepat, tetapi cenderung cepat panas.'},
 {'aspects': ['Ohjelma', 'käyttöliittymä'],
  'confidence': [0.5031888484954834, 0.48775985836982727],
  'details': [{'aspect': 'Ohjelma',
               'confidence': 0.5031888484954834,
               'end': 7,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5031888484954834,
               'probability': {'Negative': 0.2484055757522583,
                               'Neutral': 0.5031888484954834,
                               'Positive': 0.2484055757522583},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'käyttöliittymä',
               'confidence': 0.48775985836982727,
               'end': 40,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.48775985836982727,
               'probability': {'Negative': 0.25612007081508636,
                               'Neutral': 0.48775985836982727,
                               'Positive': 0.25612007081508636},
               'sentiment': 'Neutral',
               'start': 25}],
  'positions': [[0, 7], [25, 40]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': 'Ohjelma on tehokas, mutta käyttöliittymä on sekava.'},
 {'aspects': ['Maden', 'portion'],
  'confidence': [0.4489361047744751, 0.544648289680481],
  'details': [{'aspect': 'Maden',
               'confidence': 0.4489361047744751,
               'end': 5,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.4489361047744751,
               'probability': {'Negative': 0.27553194761276245,
                               'Neutral': 0.4489361047744751,
                               'Positive': 0.27553194761276245},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'portion',
               'confidence': 0.544648289680481,
               'end': 29,
               'extractor_label': 'ASP-Negative',
               'extractor_score': 0.544648289680481,
               'probability': {'Negative': 0.544648289680481,
                               'Neutral': 0.22767585515975952,
                               'Positive': 0.22767585515975952},
               'sentiment': 'Negative',
               'start': 21}],
  'positions': [[0, 5], [21, 29]],
  'sentiments': ['Neutral', 'Negative'],
  'text': 'Maden var lækker, men portionerne var for små.'},
 {'aspects': ['Počítač', 'software'],
  'confidence': [0.507230281829834, 0.5066105723381042],
  'details': [{'aspect': 'Počítač',
               'confidence': 0.507230281829834,
               'end': 7,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.507230281829834,
               'probability': {'Negative': 0.246384859085083,
                               'Neutral': 0.507230281829834,
                               'Positive': 0.246384859085083},
               'sentiment': 'Neutral',
               'start': 0},
              {'aspect': 'software',
               'confidence': 0.5066105723381042,
               'end': 31,
               'extractor_label': 'ASP-Neutral',
               'extractor_score': 0.5066105723381042,
               'probability': {'Negative': 0.24669471383094788,
                               'Neutral': 0.5066105723381042,
                               'Positive': 0.24669471383094788},
               'sentiment': 'Neutral',
               'start': 22}],
  'positions': [[0, 7], [22, 31]],
  'sentiments': ['Neutral', 'Neutral'],
  'text': 'Počítač je rychlý, ale software je zastaralý.'}]
"""