metadata

datasets:
  - sentiment-analysis-dataset-oversampled
language:
  - en
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
tags:
  - sentiment-analysis
  - text-classification
  - balanced-dataset
  - oversampling
  - csv
pretty_name: Sentiment Analysis Dataset OverSampled
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype: int64
  splits:
    - name: train
      num_examples: 121374
    - name: validation
      num_examples: 10499
    - name: test
      num_examples: 10499
  format: csv

Sentiment Analysis Dataset

Overview

This dataset is designed for sentiment analysis tasks, offering a balanced and pre-processed collection of labeled text data. The dataset includes three sentiment labels:

0: Negative
1: Neutral
2: Positive

The training dataset has been oversampled to ensure balanced label distribution, making it suitable for training robust sentiment analysis models. The validation and test datasets remain unaltered to preserve the original label distribution for unbiased evaluation.

Dataset Details

Structure

Split	Rows	Label Distribution
Train	121,374	Balanced (Oversampled)
Validation	10,499	Original
Test	10,499	Original

File Format

Type: CSV
Columns:
- text: The input text.
- label: The sentiment label (0, 1, 2).

Labels

Label	Sentiment
`0`	Negative
`1`	Neutral
`2`	Positive

Preprocessing

The dataset has been thoroughly cleaned and pre-processed, ensuring consistency and readiness for use in machine learning tasks. Preprocessing steps include:

Removal of dubplicates.
Removal of Null rows.
Filtering out extremely short or long entries.

Usage

Loading the Dataset

The dataset can be loaded and processed using common Python libraries like pandas:

import pandas as pd

# Load the train dataset
train_data = pd.read_csv("path/to/train.csv")
val_data = pd.read_csv("path/to/validation.csv")
test_data = pd.read_csv("path/to/test.csv")

# Display sample data
print(train_data.head())

Example

# Example usage: Distribution of labels in the train dataset
print(train_data['label'].value_counts())

# Example data preprocessing for modeling
from sklearn.model_selection import train_test_split
X_train, y_train = train_data['text'], train_data['label']
X_val, y_val = val_data['text'], val_data['label']
X_test, y_test = test_data['text'], test_data['label']

Applications

This dataset can be used for various sentiment analysis tasks, including:

Building sentiment classifiers for social media analysis.
Evaluating product or service feedback.
Developing opinion mining systems.

Dataset Split

Split	Purpose
Train	For model training (oversampled).
Validation	For hyperparameter tuning.
Test	For final model evaluation.

Citation

If you use this dataset in your research or projects, please provide proper attribution:

Sentiment Analysis Dataset (OverSampled) 
Contributed by: Syed Khalid Hussain

Author: Syed Khalid Hussain