Create README.md

README.md

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+---
+datasets:
+  - sentiment-analysis-dataset
+language:
+  - en
+task_categories:
+  - text-classification
+task_ids:
+  - sentiment-classification
+tags:
+  - sentiment-analysis
+  - text-classification
+  - balanced-dataset
+  - oversampling
+  - csv
+pretty_name: Sentiment Analysis Dataset (Unbalanced)
+dataset_info:
+  features:
+    - name: text
+      dtype: string
+    - name: label
+      dtype: int64
+  splits:
+    - name: train
+      num_examples: 83989
+    - name: validation
+      num_examples: 10499
+    - name: test
+      num_examples: 10499
+  format: csv
+---
+
+
+# Sentiment Analysis Dataset
+
+## Overview
+
+This dataset is designed for sentiment analysis tasks, providing labeled examples across three sentiment categories:
+- **0**: Negative
+- **1**: Neutral
+- **2**: Positive
+
+It is suitable for training, validating, and testing text classification models in tasks such as social media sentiment analysis, customer feedback evaluation, and opinion mining.
+
+---
+
+## Dataset Details
+
+### Key Features
+
+- **Type**: CSV
+- **Language**: English
+- **Labels**: 
+  - `0`: Negative 
+  - `1`: Neutral 
+  - `2`: Positive
+- **Pre-processing**:
+  - Duplicates removed
+  - Null values removed
+  - Cleaned for consistency
+
+### Dataset Split
+
+| Split        | Rows   |
+|--------------|--------|
+| **Train**    | 83,989 |
+| **Validation** | 10,499 |
+| **Test**     | 10,499 |
+
+### Format
+
+Each row in the dataset consists of the following columns:
+- `text`: The input text data (e.g., sentences, comments, or tweets).
+- `label`: The corresponding sentiment label (`0`, `1`, or `2`).
+
+---
+
+## Usage
+
+### Installation
+
+Download the dataset from the [Hugging Face Hub](https://huggingface.co/datasets/your-dataset-path) or your preferred storage location.
+
+### Loading the Dataset
+
+#### Using Pandas
+
+```python
+import pandas as pd
+
+# Load the train dataset
+train_df = pd.read_csv("path_to_train.csv")
+print(train_df.head())
+
+# Columns: text, label
+```
+
+#### Using Hugging Face's `datasets` Library
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("your-dataset-path")
+
+# Access splits
+train_data = dataset["train"]
+validation_data = dataset["validation"]
+test_data = dataset["test"]
+
+# Example: Printing a sample
+print(train_data[0])
+```
+
+---
+
+## Example Usage
+
+Here’s an example of using the dataset to fine-tune a sentiment analysis model with the [Hugging Face Transformers](https://huggingface.co/transformers) library:
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
+from datasets import load_dataset
+
+# Load dataset
+dataset = load_dataset("your-dataset-path")
+
+# Load model and tokenizer
+model_name = "bert-base-uncased"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=3)
+
+# Tokenize dataset
+def tokenize_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Prepare training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    save_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+    load_best_model_at_end=True,
+)
+
+# Initialize Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+)
+
+# Train model
+trainer.train()
+```
+
+---
+
+## Applications
+
+This dataset can be used for:
+1. **Social Media Sentiment Analysis**: Understand the sentiment of posts or tweets.
+2. **Customer Feedback Analysis**: Evaluate reviews or feedback.
+3. **Product Sentiment Trends**: Monitor public sentiment about products or services.
+
+---
+
+## License
+
+This dataset is released under the **[Insert Your Chosen License Here]**. Ensure proper attribution if used in academic or commercial projects.
+
+---
+
+## Citation
+
+If you use this dataset, please cite it as follows:
+
+```
+@dataset{your_name_2024,
+  title        = {Sentiment Analysis Dataset},
+  author       = {Syed Khalid Hussain},
+  year         = {2024},
+  url          = {https://huggingface.co/datasets/syedkhalid076/Sentiment-Analysis}
+}
+```
+
+---
+
+## Acknowledgments
+
+This dataset was curated and processed by **Syed Khalid Hussain**. The author takes care to ensure high-quality data, enabling better model performance and reproducibility.
+
+---
+
+**Author**: Syed Khalid Hussain