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Pattern-Based Emotion Classification on Social Media Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

Authors : Gizem Gezici, Berrin Yanikoglu, Dilek Tapucu, Yücel Saygın

Published in: Advances in Social Media Analysis

Publisher: Springer International Publishing

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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 the TripAdvisor database.

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Metadata
Title
Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis
Authors
Gizem Gezici
Berrin Yanikoglu
Dilek Tapucu
Yücel Saygın
Copyright Year
2015
Book
Advances in Social Media Analysis

Print ISBN: 978-3-319-18457-9

Electronic ISBN: 978-3-319-18458-6

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-18458-6_3

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