Create README.md

README.md

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+---
+datasets:
+  - Sentiment-Analysis-Over-sampled
+language:
+  - en
+task_categories:
+  - text-classification
+task_ids:
+  - sentiment-classification
+size_categories:
+  - 100K<n<500K
+tags:
+  - sentiment-analysis
+  - text-classification
+  - balanced-dataset
+  - oversampling
+  - csv
+  - pandas
+pretty_name: Sentiment Analysis Dataset (OverSampled)
+dataset_info:
+  features:
+    - name: text
+      dtype: string
+    - name: label
+      dtype: int
+  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:  
+1. Removal of dubplicates.  
+2. Removal of Null rows.
+3. Filtering out extremely short or long entries.
+
+---
+
+## Usage  
+
+### Loading the Dataset  
+
+The dataset can be loaded and processed using common Python libraries like `pandas`:  
+
+```python
+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  
+
+```python
+# 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:  
+
+```plaintext  
+Sentiment Analysis Dataset (OverSampled) 
+Contributed by: Syed Khalid Hussain  
+```
+
+---
+
+
+**Author**: Syed Khalid Hussain