Browsing Twitter can be difficult, because people criticize the things you like. So, only look at positive tweets!
For our Deep Learning 1 class, we built a model to classify whether the sentiment of a tweet is positive or negative, filter out the negative tweets, change their sentiment to positive, and then present only positive tweets.
- Clone the repository
- In the root folder, rename "connection_info_example.csv" to "connection_info.csv"
- Fill your Twitter API credentials into connection_info.csv
- Download the Twitter Sentiment Analysis Training Corpus - click the link that reads "Twitter Sentiment Analysis Dataset"
- In get_positive_tweets.ipynb, fill in your own systems FILE_PATH and the query you want to search Twitter for into "query"
- Run get_positive_tweets.ipynb
To submit changes to this repo, please do the following steps:
- Make your changes in a new branch
- Upload your ipynb code as a .py so we can compare changes
- Open a Pull Request (PR) in Github, attempting to merge your branch into main, and request reviews from the group
- When you have approvals, merge your pull request
- Daniel Siegel - 101367445
- Michael McAllister - 101359469
- Hom Kandel - 101385341
- Eduardo Bastos de Moraes - 101345799
- Juan Clackworthy - 101372229