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https://github.com/jparedesds/nlp-sentiment-analysis-with-emojis
Sentiment analysis through twitter by identifying emojis within the texts.
https://github.com/jparedesds/nlp-sentiment-analysis-with-emojis
pandas spacy spacy-models spacy-nlp spacy-transformers
Last synced: 5 days ago
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Sentiment analysis through twitter by identifying emojis within the texts.
- Host: GitHub
- URL: https://github.com/jparedesds/nlp-sentiment-analysis-with-emojis
- Owner: jparedesDS
- License: mit
- Created: 2022-12-10T17:00:00.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2025-01-10T07:43:07.000Z (17 days ago)
- Last Synced: 2025-01-10T08:37:20.080Z (17 days ago)
- Topics: pandas, spacy, spacy-models, spacy-nlp, spacy-transformers
- Language: Jupyter Notebook
- Homepage:
- Size: 8.41 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Twitter - Sentiment Analysis with Emojis
## Project Description
This project focuses on **sentiment analysis** using emojis to classify the emotional tone of tweets. By leveraging Natural Language Processing (NLP) techniques, the project analyzes how emojis are used in tweets and their relationship with sentiment.
### Project Workflow
1. **Importing Libraries and Loading Data:**
- Libraries such as `pandas`, `numpy`, and `matplotlib` are imported.
- A dataset of tweets, including the tweet text and associated emojis, is loaded.
2. **Data Preprocessing:**
- Cleaning and normalizing the tweet texts.
- Extracting emojis and analyzing their frequency in the text.
3. **Exploratory Data Analysis (EDA):**
- Visualizing the distribution of emojis in tweets.
- Grouping tweets by accounts to show the number of tweets per account.
- Analyzing correlations between popular accounts and their emoji usage.
4. **Sentiment Modeling:**
- Using NLP techniques to classify tweets into different sentiment categories.
- Applying algorithms to predict sentiment based on the presence of emojis.
5. **Results Visualization:**
- Generating graphs and tables that illustrate:
- The relationship between emojis used and the sentiment detected.
- Emoji usage frequency by the most popular accounts.
## Libraries Used
The project utilizes the following Python libraries:
- **pandas**: For data manipulation and analysis.
- **numpy**: For numerical computations.
- **matplotlib**: For data visualization.
- **seaborn**: For enhanced visualizations.
- **re (Regex)**: For text cleaning and emoji extraction.
- **nltk**: For natural language processing.
## Key Features
- **Emoji Sentiment Analysis:** Leveraging emojis as indicators of sentiment in tweets.
- **Data-Driven Insights:** Visualizing trends in emoji usage and their sentiment implications.
- **NLP Integration:** Combining traditional NLP techniques with emoji-specific sentiment modeling.
## System Requirements
- **Python 3.x**
- Additional dependencies installed via `pip`.
## How to Run
1. Clone this repository:
```bash
git clone https://github.com/jparedesDS/twitter-sentiment-analysis-emojis.git
2. Install the required dependencies:
```
pip install -r requirements.txt
```
3. Load your dataset in the required format (CSV with columns for tweet text and emojis).
4. Run the Jupyter Notebook:
```
jupyter notebook
```
5. Open the Twitter_Sentiment_Analysis_with_Emojis.ipynb file to explore the analysis and visualizations.
## Contributions
Contributions are welcome! If you'd like to improve this project, please:
1. Fork the repository.
2. Create a new branch (git checkout -b feature/new-feature).
3. Make your changes and commit them (git commit -am 'Add new feature').
4. Push your branch (git push origin feature/new-feature).
5. Open a Pull Request.
## License
This project is licensed under the MIT License. See the LICENSE file for details.