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Feature selection for sentiment analysis based on content and syntax models

Authors:

Fei SongAuthors Info & Claims

Decision Support Systems, Volume 53, Issue 4

Pages 704 - 711

https://doi.org/10.1016/j.dss.2012.05.023

Published: 01 November 2012 Publication History

Abstract

Recent solutions for sentiment analysis have relied on feature selection methods ranging from lexicon-based approaches where the set of features are generated by humans, to approaches that use general statistical measures where features are selected solely on empirical evidence. The advantage of statistical approaches is that they are fully automatic, however, they often fail to separate features that carry sentiment from those that do not. In this paper we propose a set of new feature selection schemes that use a Content and Syntax model to automatically learn a set of features in a review document by separating the entities that are being reviewed from the subjective expressions that describe those entities in terms of polarities. By focusing only on the subjective expressions and ignoring the entities, we can choose more salient features for document-level sentiment analysis. The results obtained from using these features in a maximum entropy classifier are competitive with the state-of-the-art machine learning approaches.

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Alyoubi KSharma A(2023)Deep Recurrent Neural Model for Multi Domain Sentiment Analysis with Attention MechanismWireless Personal Communications: An International Journal10.1007/s11277-023-10274-x130:1(43-60)Online publication date: 22-Mar-2023
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Chen DZhengwei HYiting TJintao MKhanal R(2023)Emotion and sentiment analysis for intelligent customer service conversation using a multi-task ensemble frameworkCluster Computing10.1007/s10586-023-04073-z27:2(2099-2115)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04073-z
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cover image Decision Support Systems

Decision Support Systems Volume 53, Issue 4

November, 2012

229 pages

ISSN:0167-9236

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Copyright © Elsevier B.V. © 2012.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2012

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Cited By

Qin JZheng PWang X(2024)Product Redesign and Innovation Based on Online ReviewsINFORMS Journal on Computing10.1287/ijoc.2022.033336:3(742-765)Online publication date: 1-May-2024
https://dl.acm.org/doi/10.1287/ijoc.2022.0333
Alyoubi KSharma A(2023)Deep Recurrent Neural Model for Multi Domain Sentiment Analysis with Attention MechanismWireless Personal Communications: An International Journal10.1007/s11277-023-10274-x130:1(43-60)Online publication date: 22-Mar-2023
https://dl.acm.org/doi/10.1007/s11277-023-10274-x
Chen DZhengwei HYiting TJintao MKhanal R(2023)Emotion and sentiment analysis for intelligent customer service conversation using a multi-task ensemble frameworkCluster Computing10.1007/s10586-023-04073-z27:2(2099-2115)Online publication date: 22-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04073-z
Ullah AKhan SNawi N(2022)Review on sentiment analysis for text classification techniques from 2010 to 2021Multimedia Tools and Applications10.1007/s11042-022-14112-382:6(8137-8193)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-022-14112-3
Xie ZLiu GQu JWu JLi H(2022)Identifying Structural Holes for Sentiment ClassificationInformation Systems Frontiers10.1007/s10796-021-10185-x24:5(1735-1751)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10796-021-10185-x
Mousavizadeh MKoohikamali MSalehan MKim D(2022)An Investigation of Peripheral and Central Cues of Online Customer Review Voting and Helpfulness through the Lens of Elaboration Likelihood ModelInformation Systems Frontiers10.1007/s10796-020-10069-624:1(211-231)Online publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1007/s10796-020-10069-6
Wankhade MRao AKulkarni C(2022)A survey on sentiment analysis methods, applications, and challengesArtificial Intelligence Review10.1007/s10462-022-10144-155:7(5731-5780)Online publication date: 7-Feb-2022
https://dl.acm.org/doi/10.1007/s10462-022-10144-1
Amiridi MKargas NSidiropoulos N(2021)Information-Theoretic Feature Selection via Tensor Decomposition and SubmodularityIEEE Transactions on Signal Processing10.1109/TSP.2021.312514769(6195-6205)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1109/TSP.2021.3125147
Singh RSachan MPatel R(2021)360 degree view of cross-domain opinion classification: a surveyArtificial Intelligence Review10.1007/s10462-020-09884-954:2(1385-1506)Online publication date: 1-Feb-2021
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Tao JZhou L(2020)A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online ReviewsACM Transactions on Management Information Systems10.1145/339963011:3(1-22)Online publication date: 21-Jul-2020
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