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" The Evolution of Star-Forming Galaxies Using the Largest Narrowband Surveys "
Khostovan, Ali Ahmad
Mobasher, Bahram
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Latin Dissertation
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| Language of Document
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English
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| Record Number
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904415
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TL4x85k62h
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| Main Entry
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Bhattacharjee, Kasturi
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| Title & Author
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Opinion Detection, Sentiment Analysis and User Attribute Detection from Online Text Data\ Bhattacharjee, KasturiPetzold, Linda
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| College
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UC Santa Barbara
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| Date
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2016
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| student score
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2016
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| Abstract
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With the growing increase in the use of the internet in most parts of the world today, users generate significant amounts of online text on different platforms such as online social networks, product review websites, travel blogs, to name just a few. The variety of content on these platforms has made them an important resource for researchers to gauge user activity, determine their opinions and analyze their behavior, without having to perform monetarily and temporally expensive surveys. Gaining insights into user behavior enables us to better understand their likes and dislikes, which in turn is helpful for economic purposes such as marketing, advertising and recommendations. Further, owing to the fact that online social networks have recently been instrumental in socio-political revolutions such as the Arab Spring, and for awareness-generation campaigns by MoveOn.org and Avaaz.org, analysis of online data can uncover user preferences. The overarching goal of this Ph.D. thesis is to pose some research questions and propose solutions, mostly pertaining to user opinions and attributes, keeping in mind the large quantities of noise present in online textual data. This thesis illustrates that with the extraction of informative textual features and the use of robust NLP and machine learning techniques, it is possible to perform efficient signal extraction from online text data, and use it to better understand user behavior. The first research problem addressed is that of opinion detection and sentiment analysis of users on a given topic, from their self-generated tweets. The key idea is to select relevant hashtags and n-grams using an
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Petzold, Linda
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UC Santa Barbara
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