Sentiment Analysis of the Role Play World (RPW) Community

A Comprehensive Study on User Sentiments, Anonymity, and Social Interactions

Description

This project focuses on the sentiment analysis of the Role Play World (RPW) community, a unique online space where users assume different identities, often of idols or fictional characters, to interact anonymously on social media platforms like Facebook. The study aims to understand user sentiments, explore the impact of anonymity on user experiences, and investigate how the RPW community fosters social interaction and personal expression.

Features

• Sentiment Analysis: Analyzes the overall sentiment of users in the RPW community, with sentiment scores ranging from negative to very positive.
• User Experience Analysis: Investigates how different respondent types and interview sections influence sentiment.
• Impact of Anonymity: Explores how anonymity in RPW allows for open communication, self-expression, and the development of a supportive community.
• Correlation Studies: Examines the relationship between user experience (years of RPW use) and sentiment.

Table of Contents

1. Introduction
2. Installation
3. Usage
4. Data
5. Methodology
6. Results
7. Conclusion
8. Future Work
9. Contributing
10. License
11. Acknowledgements

Introduction

The RPW community is a fascinating blend of online personas and anonymity, allowing users to take on roles distinct from their real-life identities. This study delves into the sentiments of RPW users, examining how anonymity affects their interactions and the overall experience within this digital space.

Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/RPW-Sentiment-Analysis.git

2. Navigate to the project directory:

   cd RPW-Sentiment-Analysis

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

To perform sentiment analysis on your RPW dataset:

1. Ensure your dataset is in the correct format (e.g., CSV, JSON).
2. Run the analysis script:

   python analyze_sentiments.py --input data/rpw_dataset.csv

3. View the results in the output directory, where sentiment scores, visualizations, and summary statistics will be stored.

Data

The dataset used in this study contains the following columns:

• InterviewSection: The section of the interview (e.g., first interview, second interview).
• RespondentType: The type of respondent (e.g., first respondent, second respondent).
• RespondentName: The pseudonym or identifier of the respondent.
• RPWUserSince: The year the respondent started using RPW.
• Question: The questions asked during the interview.
• Answer: The responses given by the respondents.
• YearsInRPW: The number of years the respondent has been a part of RPW.
• Sentiment: The sentiment score of the respondent's answer, ranging from negative to positive.
• Emotion: The emotion detected in the respondent's answer.

Please note that the data is private and not available for public use due to confidentiality and privacy concerns. Only authorized researchers with specific access permissions may use this dataset.

Methodology

This study builds upon the qualitative findings of a previous investigation into the Role Play World (RPW) community by employing Exploratory Data Analysis (EDA) and other quantitative methods to analyze user sentiments. The methodology is designed to provide a comprehensive understanding of user experiences and sentiments within the RPW community.

Results

The analysis reveals a generally positive sentiment within the RPW community, with variations across different user groups and interview sections. The findings suggest that anonymity plays a crucial role in facilitating open communication and positive interactions.

Conclusion

The RPW community offers a unique platform for users to express themselves freely, often leading to positive social interactions and personal growth. This study highlights the importance of anonymity in creating a supportive online environment.

Future Work

Further research could explore the long-term effects of RPW participation on user behavior, the impact of anonymity on mental health, and the potential for using sentiment analysis to improve online community experiences.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request if you'd like to contribute to this project.

License

See the LICENSE file for details.

Acknowledgements

Special thanks to the RPW community members for participating in this study and to all contributors who helped make this project possible.