Text mining of Twitter data using an LDA topic model and sentiment analysis

Yang, S. (2018). Text mining of Twitter data using an LDA topic model and sentiment analysis. https://scholar.acadiau.ca/islandora/object/theses:2656

Title: Text mining of Twitter data using an LDA topic model and sentiment analysis
Author: Yang, Sidi
Call Number: LE3 .A278 2018
Date: 2018
Supervisor: Zhang, Haiyi
Degree Grantor: Acadia University
Degree Name: Bachelor of Computer Science
Degree Level: Honours
Discipline: Computer Science
Affiliation: Computer Science
Abstract: 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.
Rights: The author retains copyright in this thesis. Any substantial copying or any other actions that exceed fair dealing or other exceptions in the Copyright Act require the permission of the author.
Permanent Link: https://scholar.acadiau.ca/islandora/object/theses:2656

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