Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5677
Title: Sentiment analysis using domain-adaptation and sentence-based analysis
Authors: Gezici, Gizem
Yanıkoğlu, Berrin
Tapucu, Dilek
Saygın, Yücel
Tapucu, Dilek
Izmir Institute of Technology. Computer Engineering
Keywords: Sentiment analysis
Polarity lexicons
sentiment classification
TripAdvisor
Issue Date: 2015
Publisher: Springer Verlag
Source: Gezici, G., Yanıkoğlu, B., Tapucu, D., and Saygın, Y. (2015). Sentiment analysis using domain-adaptation and sentence-based analysis. Studies in Computational Intelligence, 602, 45-64. doi:10.1007/978-3-319-18458-6_3
Abstract: Sentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We apply a simple method to adapt a general purpose polarity lexicon to a specific domain [1]. Subsequently, 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 for 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.We use a subset of hotel reviews from the TripAdvisor database [2] to evaluate the effect of sentence-level features on sentiment classification. Then, we measure the performance of our sentiment analysis engine using the domain-adapted lexicon on a large subset of theTripAdvisor database.
URI: http://doi.org/10.1007/978-3-319-18458-6_3
http://hdl.handle.net/11147/5677
ISSN: 1860-949X
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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