This project involves a Twitter Bot that performs sentiment analysis on tweets. The bot listens for requests on Twitter and posts back an image of the analysis to the user who requested it.

The project uses the following libraries:

• tweepy for interacting with the Twitter API
• json for handling JSON data
• pandas and numpy for data manipulation
• matplotlib for plotting sentiment analysis results
• datetime for handling date and time
• os for environment variable management
• time for managing intervals between bot actions
• vaderSentiment for sentiment analysis

The bot uses Twitter API keys (consumer key, consumer secret, access token, and access token secret) to authenticate and interact with Twitter.

When the bot receives a mention, it identifies and parses the tweet to extract the screen names of users for whom sentiment analysis is requested.

The bot cleanses the tweets to remove noise (like mentions, URLs, and media links) and uses the VADER sentiment analysis tool to analyze the sentiment of each tweet. The analysis results include a compound score indicating the overall sentiment.

The bot plots the sentiment scores using matplotlib. It generates a line plot with sentiment scores for each tweet, where positive sentiments are marked in green and negative sentiments in red.

The bot continuously scans for new mentions on Twitter. When it finds a mention, it processes the request by performing sentiment analysis on the recent tweets of the requested users. It then posts the sentiment analysis results back to the user who requested it, along with an image of the plot.

The bot runs in an infinite loop, periodically scanning for new requests and processing them.

The bot provides sentiment analysis for users like WSJ and CocaCola, displaying the number of tweets analyzed and the resulting sentiment plots.

• WSJ - 200 Tweets Analyzed

• CocaCola - 200 Tweets Analyzed