The Tweet Sentiment Analysis App is a Python application that streams tweets from Twitter, performs sentiment analysis on the collected tweets, and stores the sentiment data in an Elasticsearch database. The application utilizes the Tweepy library for Twitter data streaming, the TextBlob library for sentiment analysis, and the Elasticsearch library for data storage.
- Streams tweets based on specified topics using the Tweepy library.
- Performs sentiment analysis on each tweet using the TextBlob library.
- Determines whether a tweet's sentiment is positive, negative, or neutral.
- Stores sentiment data, tweet content, and author information in an Elasticsearch database.
- Python 3.6+
- Tweepy library
- TextBlob library
- Elasticsearch library
- Access to Twitter API (requires Twitter Developer account)
- Access to Google Maps Geocoding API (for location-based analysis)
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Clone the repository to your local machine.
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Install the required libraries using pip:
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Create a Twitter Developer account and obtain API keys and access tokens.
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Update the necessary API keys and tokens in the code (e.g.,
config.py). -
Optionally, set up a Google Maps Geocoding API key for location-based analysis.
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Make sure Elasticsearch is running and accessible (adjust the Elasticsearch URL in the code if needed).
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Run the
main()function in the code to start streaming tweets, performing sentiment analysis, and storing data in Elasticsearch.
- Tweepy: Python library for accessing the Twitter API.
- TextBlob: Python library for processing textual data.
- Elasticsearch: Distributed search and analytics engine.
- Google Maps Geocoding API: For location-based analysis.
This project is licensed under the MIT License.
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