Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Author: search.filters.author.Yanıkoğlu, BerrinSubject: search.filters.subject.Polarity detection

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  • Conference Object
    Citation - WoS: 22
    Citation - Scopus: 29
    Learning Domain-Specific Polarity Lexicons
    (Institute of Electrical and Electronics Engineers Inc., 2012) Demiröz, Gülşen; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, Yücel
    Sentiment analysis aims to automatically estimate the sentiment in a given text as positive or negative. Polarity lexicons, often used in sentiment analysis, indicate how positive or negative each term in the lexicon is. However, since creating domain-specific polarity lexicons is expensive and time-consuming, researchers often use a general purpose or domain-independent lexicon. In this work, we address the problem of adapting a general purpose polarity lexicon to a specific domain and propose a simple yet effective adaptation algorithm. We experimented with two sets of reviews from the hotel and movie domains and observed that while our adaptation techniques changed the polarity values for only a small set of words, the overall test accuracy increased significantly: 77% to 83% in the hotel dataset and 61% to 66% in the movie dataset. © 2012 IEEE.
  • Conference Object
    Citation - Scopus: 18
    New Features for Sentiment Analysis: Do Sentences Matter?
    (CEUR Workshop Proceedings, 2012) Gezici, Gizem; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, Yücel
    In this work, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step before estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. The TripAdvisor dataset is used to evaluate the effect of sentence level features on polarity classification. Our initial results indicate a small improvement in classification accuracy when using the newly proposed features. However, the benefit of these features is not limited to improving sentiment classification accuracy since sentence level features can be used for other important tasks such as review summarization.