A Sentiment-Aware Topic Model for Extracting Failures from Product Reviews

This paper describes a probabilistic model that aims to extract different kinds of product difficulties conditioned on users’ dissatisfaction through the use of sentiment information. The proposed model learns a distribution over words, associated with topics, sentiment and problem labels. The resul...

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Published inText, Speech, and Dialogue pp. 37 - 45
Main Author Tutubalina, Elena
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Information extraction
LDA
Mining product defects
Opinion mining
Problem phrase extraction
Topic modeling
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Abstract This paper describes a probabilistic model that aims to extract different kinds of product difficulties conditioned on users’ dissatisfaction through the use of sentiment information. The proposed model learns a distribution over words, associated with topics, sentiment and problem labels. The results were evaluated on reviews of products, randomly sampled from several domains (automobiles, home tools, electronics, and baby products), and user comments about mobile applications, in English and Russian. The model obtains a better performance than several state-of-the-art models in terms of the likelihood of a held-out test and outperforms these models in a classification task.
AbstractList This paper describes a probabilistic model that aims to extract different kinds of product difficulties conditioned on users’ dissatisfaction through the use of sentiment information. The proposed model learns a distribution over words, associated with topics, sentiment and problem labels. The results were evaluated on reviews of products, randomly sampled from several domains (automobiles, home tools, electronics, and baby products), and user comments about mobile applications, in English and Russian. The model obtains a better performance than several state-of-the-art models in terms of the likelihood of a held-out test and outperforms these models in a classification task.
Author Tutubalina, Elena
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Copyright Springer International Publishing Switzerland 2016
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DOI 10.1007/978-3-319-45510-5_5
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Discipline Engineering
Computer Science
EISBN 9783319455105
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EISSN 1611-3349
Editor Pala, Karel
Horák, Aleš
Kopeček, Ivan
Sojka, Petr
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Snippet This paper describes a probabilistic model that aims to extract different kinds of product difficulties conditioned on users’ dissatisfaction through the use...
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StartPage 37
SubjectTerms Information extraction
LDA
Mining product defects
Opinion mining
Problem phrase extraction
Topic modeling
Title A Sentiment-Aware Topic Model for Extracting Failures from Product Reviews
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