Author Type

Graduate Student

Date of Award

2017

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Shankar, Ravi

Abstract

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.

Extent

62 p.

Comments

Includes bibliography.|Thesis (M.S.)--Florida Atlantic University, 2017.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00004877

Format

pdf

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