Computer Engineering / Bilgisayar Mühendisliği

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

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Publisher: search.filters.publisher.Institute of Electrical and Electronics Engineers Inc.Subject: search.filters.subject.Machine learning

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  • Conference Object
    Citation - WoS: 18
    Citation - Scopus: 25
    Adaptation and Use of Subjectivity Lexicons for Domain Dependent Sentiment Classification
    (Institute of Electrical and Electronics Engineers Inc., 2012) Dehkharghani, Rahim; Yanıkoğlu, Berrin; Tapucu, Dilek; Saygın, Yücel
    Sentiment analysis refers to the automatic extraction of sentiments from a natural language text. We study the effect of subjectivity-based features on sentiment classification on two lexicons and also propose new subjectivity-based features for sentiment classification. The subjectivity-based features we experiment with are based on the average word polarity and the new features that we propose are based on the occurrence of subjective words in review texts. Experimental results on hotel and movie reviews show an overall accuracy of about 84% and 71% in hotel and movie review domains respectively; improving the baseline using just the average word polarities by about 2% points. © 2012 IEEE.
  • 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.