Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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2012 - 20133

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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 - Scopus: 19
    Data Mining for Microrna Gene Prediction: on the Impact of Class Imbalance and Feature Number for Microrna Gene Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2013) Saçar, Müşerref Duygu; Allmer, Jens
    MicroRNAs (miRNAs) are small, non-coding RNAs which are involved in the posttranscriptional modulation of gene expression. Their short (18-24) single stranded mature sequences are involved in targeting specific genes. It turns out that experimental methods are limited and that it is difficult, if not impossible, to establish all miRNAs and their targets experimentally. Therefore, many tools for the prediction of miRNA genes and miRNA targets have been proposed. Most of these tools are based on machine learning methods and within that area mostly two-class classification is employed. Unfortunately, truly negative data is impossible to attain and only approximations of negative data are currently available. Also, we recently showed that the available positive data is not flawless. Here we investigate the impact of class imbalance on the learner accuracy and find that there is a difference of up to 50% between the best and worst precision and recall values. In addition, we looked at increasing number of features and found a curve maximizing at 0.97 recall and 0.91 precision with quickly decaying performance after inclusion of more than 100 features. © 2013 IEEE.
  • 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.