Data • python • plotly • pandas • LDA • nltk •TEXTBLOB• html • css
This is a master's capstone that examines the quality of life in geographical areas of tweets from Twitter's API. The methodological approach to this is using the keyword, why, regarding the World Health Organization's definition of quality of life for the query.
Python and Plotly were used to combine this project with the following concepts: text analysis, topic modeling using Latent Dirichlet Allocation (LDA), and sentiment analysis (NLTK and TextBlob). Topic modeling was used to extract primary "topics" and associated words. Sentiment analysis was used to determine how tweeters felt overall.
Below are some images of the analysis. The live website for this project is here. The GitHub repository is here.
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