task_categories:text-classification language:en license:cc-by-nc-sa-4.0 region:us
---
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- en
---

# Sentiment Analysis Dataset for Electronic Product Reviews

## Dataset Overview

This dataset contains annotated reviews of  Electronic Product Review . Each review includes metadata such as the reviewer's name, the text of the review, the source URL, and sentiment annotations from two annotators. The dataset is designed for training and evaluating sentiment analysis models, with a focus on multi-class sentiment classification (Positive, Neutral, Negative).

### Dataset Features

- **ID**: Unique identifier for each review.
- **Reviewer Name**: The username of the reviewer 
- **Review Text**: The content of the review.
- **Source**: URL of the Reddit comment or post where the review was collected.
- **Date of Collection**: The date when the review was collected.
- **Annotator_1**: Sentiment label assigned by the first annotator.
- **Annotator_2**: Sentiment label assigned by the second annotator.
- **Ground_Truth**: Final sentiment label based on the agreement between annotators.

### Dataset Statistics

- **Number of Reviews**: 200 

## Dataset Creation

The dataset was curated by collecting user reviews from Reddit. Two annotators independently labeled each review for sentiment, and disagreements were resolved to create the ground truth labels. Below is the process:

1. **Data Collection**: Reviews were extracted using Reddit's API.
2. **Annotation**: Each review was labeled by two annotators as Positive, Neutral, or Negative.
3. **Ground Truth Generation**: Final labels were determined based on annotator agreement 


## Usage

The dataset is split into two subsets:
- **Train Set**: Used for model training.
- **Test Set**: Used for evaluation and leaderboard scoring.

You can use this dataset for any research or application focusing on sentiment analysis. Ensure proper attribution if you publish results using this dataset.

## Performance Benchmarks

This dataset was used in a [Codalab competition](https://codalab.lisn.upsaclay.fr/competitions/20740?secret_key=24494bac-3fbb-4e27-9cad-f5a2a59dab33) to evaluate model performance. Below are the top 3 performers on the leaderboard:

| Rank | User         | Entries | Date of Last Entry | Accuracy | F1-Score | Precision | Recall | Detailed Results |
|------|--------------|---------|--------------------|----------|----------|-----------|--------|------------------|
| 1    | ayoubsa      | 9       | 11/19/2024         | 0.8500   | 0.53     | 0.61      | 0.50   | [View](https://codalab.lisn.upsaclay.fr/competitions/20740?secret_key=24494bac-3fbb-4e27-9cad-f5a2a59dab33#results) |
| 2    | rysmith      | 1       | 11/22/2024         | 0.8500   | 0.53     | 0.61      | 0.50   | [View](https://codalab.lisn.upsaclay.fr/competitions/20740?secret_key=24494bac-3fbb-4e27-9cad-f5a2a59dab33#results) |
| 3    | lochan9999   | 3       | 11/21/2024         | 0.8500   | 0.53     | 0.61      | 0.50   | [View](https://codalab.lisn.upsaclay.fr/competitions/20740?secret_key=24494bac-3fbb-4e27-9cad-f5a2a59dab33#results) |

### Baseline Performance

- **Accuracy**: 0.8500
- **F1-Score**: 0.53
- **Precision**: 0.61
- **Recall**: 0.50

The leaderboard metrics serve as a benchmark for evaluating new models trained on this dataset.

## Licensing

This dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.



## Contact

For any questions or issues, please contact [chawki@umich.edu](mailto:chawki@umich.edu).