This project analyzes Reddit posts about various companies to understand public sentiment, compare discussions, and identify key topics using Natural Language Processing (NLP) techniques.
The goal of this project is to extract Reddit posts related to specific companies and perform sentiment and text analysis to uncover insights such as:
- How people feel about each company (positive, negative, neutral)
- Common themes and frequently discussed topics
- Comparison of public perception across multiple companies
- Data Extraction: Collected Reddit posts mentioning company names and related discussions.
- Text Preprocessing: Used spaCy for tokenization, lemmatization, and normalization.
- Feature Engineering: Applied TF-IDF vectorization to represent textual data numerically.
- Sentiment Analysis: Classified sentiments using BERT-based models.
- Comparative Analysis: Compared sentiment trends across companies to identify public perception differences.
| Category | Tools / Libraries |
|---|---|
| Programming Language | Python |
| NLP | spaCy, Transformers |
| Sentiment Models | BERT (CardiffNLP RoBERTa) |
| Vectorization | TF-IDF (scikit-learn) |
| Data Handling | pandas, NumPy |
| Visualization | matplotlib, seaborn |
- Data Collection: Extracted Reddit posts related to selected companies.
- Text Cleaning & Normalization: Removed noise, punctuation, and stopwords.
- Lemmatization & Tokenization: Processed text using spaCy’s NLP pipeline.
- TF-IDF Vectorization: Converted clean text into numerical features.
- Sentiment Classification: Used sentiment models to label each post.
- Analysis & Visualization: Compared sentiment trends and top keywords.
- Classified Reddit posts into Positive, Negative, and Neutral categories.
- Identified top keywords and themes for each company.
- Compared sentiment ratios to reveal public perception trends.
- Add topic modeling using LDA or BERTopic
- Deploy as an interactive dashboard (Streamlit/Plotly Dash)
Contributions are welcome! Feel free to open issues or submit pull requests to improve the project.
This project is licensed under the MIT License.