Sentiment Analysis has been researched in a variety of contexts but in this thesis, the focus is on sentiment analysis in Twitter, which poses its own unique challenges such as the use of slang, abbreviations, emoticons, hashtags, and user mentions. The 140-character restriction on the length of tweets can also lead to text that is difficult even for a human to determine its sentiment. Specifically, this study will analyze sentiment analysis of bilingual (U.S. English and Spanish language) Tweets. The hypothesis here is that Bilingual sentiment analysis is more accurate than sentiment analysis in a single language (English or Spanish) when analyzing bilingual tweets. In general, currently sentiment analysis in bilingual tweets is done against an English dictionary. For each of the test cases in this thesis’ experiment we will use the Python NLTK sentiment package.
|Commitee:||Neelakanta, Perambur, Zhu, Xingquan|
|School:||Florida Atlantic University|
|School Location:||United States -- Florida|
|Source:||MAI 56/04M(E), Masters Abstracts International|
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