Fine-Grained Opinion Mining from Mobile App Reviews with Word Embedding Features

Existing approaches for opinion mining mainly focus on reviews from Amazon, domain-specific review websites or social media. Little efforts have been spent on fine-grained analysis of opinions in review texts from mobile smart phone applications. In this paper, we propose an aspect and subjective ph...

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Bibliographic Details
Published inNatural Language Processing and Information Systems Vol. 10260; pp. 3 - 14
Main Authors Sänger, Mario, Leser, Ulf, Klinger, Roman
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2017
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
App reviews
German
Opinion mining
Reviews
Sentiment analysis
Online AccessGet full text
ISBN3319595687
9783319595689
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-59569-6_1

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Summary:Existing approaches for opinion mining mainly focus on reviews from Amazon, domain-specific review websites or social media. Little efforts have been spent on fine-grained analysis of opinions in review texts from mobile smart phone applications. In this paper, we propose an aspect and subjective phrase extraction model for German reviews from the Google Play store. We analyze the impact of different features, including domain-specific word embeddings. Our best model configuration shows a performance of 0.63 $$F_1$$ for aspects and 0.62 $$F_1$$ for subjective phrases. Further, we perform cross-domain experiments: A model trained on Amazon reviews and tested on app reviews achieves lower performance (drop by 27% points for aspects and 15% points for subjective phrases). The results indicate that there are strong differences in the way personal opinions on product aspects are expressed in the particular domains.
Bibliography:Original Abstract: Existing approaches for opinion mining mainly focus on reviews from Amazon, domain-specific review websites or social media. Little efforts have been spent on fine-grained analysis of opinions in review texts from mobile smart phone applications. In this paper, we propose an aspect and subjective phrase extraction model for German reviews from the Google Play store. We analyze the impact of different features, including domain-specific word embeddings. Our best model configuration shows a performance of 0.63 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} for aspects and 0.62 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} for subjective phrases. Further, we perform cross-domain experiments: A model trained on Amazon reviews and tested on app reviews achieves lower performance (drop by 27% points for aspects and 15% points for subjective phrases). The results indicate that there are strong differences in the way personal opinions on product aspects are expressed in the particular domains.
ISBN:3319595687
9783319595689
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-59569-6_1