Inspiration
Expression: One of the first steps of mental health and well being A common fear among people posting on social media is that someone will have a strong negative reaction to something you share. These comments have a negative impact on the mood of the user. What if there are negative comments on a user posting about mental health problems. It may push them over the edge.
What it does
The tool finds tweets about Mental Health and collects replies for each tweet. If the tweet has an overall of negative replies, a chatbox replies to the user tweet with a positive message to help them feel better.
How we built it
We used Python to scrape tweets and replies and used NLP neural network model to predict the negativity of the replies with their confidence values. Then, we used selenium to create a bot that can navigate the tweets with negativity and can reply with a positive message to that tweet.
Challenges we ran into
Scraping the replies to each tweet was very challenging and time-consuming.
Accomplishments that we're proud of
We were able to navigate tweets based on the topic "Mental Health" and scrape comments for each of the tweets.
What we learned
We learned that there are some tweets where people express negativity and that needs to be addressed. We learnt a ton of new libraries, packages and tools such as Deepnote and Selenium.
What's next for Tweety
Generalizing the tool to study the user tweet replies on any topics Expanding it to other social media sites. Develop a virtual chatbot that can reply positively to the tweets of users with more negativity.
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