Eick, Christoph F.2020-06-022020-06-02May 20202020-05May 2020https://hdl.handle.net/10657/6617The process of gathering insights from social media has gained significant importance in the last decade. Since social media data is growing larger and larger, frameworks that can analyze social media content automatically are of critical importance. Twitter is a micro-blog service that generates a massive amount of textual content every day. Throughout our research, we concentrate on using Twitter for the task of sentiment analysis, the most popular micro-blogging site. We demonstrate how to compile a corpus automatically for purposes of sentiment analysis and opinion mining. Sentiment analysis classifies texts based on the sentimental orientation of opinions and emotions they contain. In this project, we are interested in evaluating popular sentiment analysis tools that automatically determine emotions in tweets and to develop computational methods that summarize the content of a large set of tweets. For the comparison of sentiment analysis tools, we created different benchmarks of manually annotated tweet datasets, and then evaluated the tools using these benchmarks. We also addressed some of the most popular sentiment analysis challenges. As far as summarization of tweets is concerned, we designed and developed algorithms that extract keywords and key sentences as a summary for a set of tweets. Finally, we developed a tool that creates a distance matrix for a set of tweets relying on the popular TF-IDF framework.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).NLP, Twitter Analytics, Tweet Summarization, Sentiment Analysis2020-06-02Thesisborn digital