Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Applying machine learning and natural language processing techniques to twitter sentiment classification for Turkish and English

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dc.contributor Graduate Program in Electrical and Electronic Engineering.
dc.contributor.advisor Saraçlar, Murat.
dc.contributor.advisor Özgür, Arzucan.
dc.contributor.author Ayata, Değer.
dc.date.accessioned 2023-03-16T10:19:19Z
dc.date.available 2023-03-16T10:19:19Z
dc.date.issued 2018.
dc.identifier.other EE 2018 A83
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12920
dc.description.abstract Determining the feelings of a user on a topic is called sentiment analysis. Senti ment analysis is done so as not just to get a sort of feedback data for decision making but also to check the emotions, characteristics and influence of situation actions, news or events on users or specific data with respect to users. Sentiment analysis of a sen tence is the effort of understanding whether the sentence contains positive, negative or neutral meaning and is used to gather overall attitude toward a product, person or event etc. In this thesis we have studied the effectiveness of applying machine learn ing and statistical natural language processing techniques collaboratively to Twitter messages based sentiment classification problem. This thesis covers the work on Turk ish tweet sentiment analysis, sector based sentiment analysis, English tweet sentiment analysis, and political opinion prediction. We introduce sector based sentiment analysis framework by applying machine learning and statistical natural language techniques collaboratively. We apply our framework to finance, telecom, retail and sport sectors. In English sentiment analysis, this thesis covers our solution for the SemEval Twitter Sentiment Analysis task. Consecutively, in English Twitter analysis our work also ad dresses the task of political orientation classification based on Twitter data. We have used various machine learning algorithms that can predict and automatically classify whether a tweet belongs to a republican or a democrat. We have used a Twitter dataset which consists of democrat and republican voters tweets. This work covers political opinion/sentiment tendency estimation based on Twitter messages of voters.
dc.format.extent 30 cm.
dc.publisher Thesis (M.A.) - Bogazici University. Institute for Graduate Studies in the Social Sciences, 2018.
dc.subject.lcsh Machine learning.
dc.subject.lcsh Twitter.
dc.subject.lcsh Natural language processing (Computer science)
dc.title Applying machine learning and natural language processing techniques to twitter sentiment classification for Turkish and English
dc.format.pages xii, 58 leaves ;


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