Text mining of Twitter data using an LDA topic model and sentiment analysis
LE3 .A278 2018
Bachelor of Computer Science
I explore text mining of Twitter plain text data in English using Latent Dirichlet Allocation topic modelling and sentiment analysis. Using a probabilistic LDA topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood.
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