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dc.contributor.advisor Thoms, Dr. Brian
dc.contributor.author Lu, Yite
dc.date.accessioned 2019-07-18T16:51:25Z
dc.date.available 2019-07-18T16:51:25Z
dc.date.issued 2019-05
dc.identifier.uri http://hdl.handle.net/10211.3/212043
dc.description.abstract Sentiment Analysis is a popular topic in machine learning, a subfield of computer science. In the past, Sentiment Analysis has been widely adopted in e-commerce and helps organizations analyze customer satisfaction of products and services. More recently, Sentiment Analysis has expanded its applications across government agencies as well, whether it being to analyze potential human threats within social media or political influence in election campaigns. Even more generally, humans are simply curious about how other humans are feeling. Two major approaches to Sentiment Analysis include lexical semantic analysis and machine learning. In this thesis, I will combine different word embedding techniques and use machine learning to analyze sentiments across published tweets. The overall goal is to discover which approach to Sentiment Analysis offers better performance. en_US
dc.format.extent 100 en_US
dc.language.iso en_US en_US
dc.publisher California State University Channel Islands en_US
dc.subject Computer Science thesis en_US
dc.subject NLP en_US
dc.subject Machine Learning en_US
dc.subject Sentiment Analysis en_US
dc.title Performing Sentiment Analysis on Tweets: A Comparison of Machine Learning Algorithms Across Large Data Sets en_US
dc.type Thesis en_US
dc.contributor.committeeMember Soltys, Dr. Michael
dc.contributor.committeeMember Isaacs, Dr. Jason
dc.contributor.committeeMember Özturgut, Dr. Osman

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