Movie rating and review summarization in mobile environment

Chien-Liang Liu; Wen Hoar Hsaio; Chia-Hoang Lee; Gen Chi Lu; Emery Jou

doi:10.1109/TSMCC.2011.2136334

Movie rating and review summarization in mobile environment

Chien-Liang Liu^*, Wen Hoar Hsaio, Chia-Hoang Lee, Gen Chi Lu, Emery Jou

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

109 Scopus citations

Abstract

In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.

Original language	English
Article number	5759102
Pages (from-to)	397-407
Number of pages	11
Journal	IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Volume	42
Issue number	3
DOIs	https://doi.org/10.1109/TSMCC.2011.2136334
State	Published - May 2012

Keywords

Feature extraction
natural language processing (NLP)
text analysis
text mining

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10.1109/TSMCC.2011.2136334

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title = "Movie rating and review summarization in mobile environment",

abstract = "In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.",

keywords = "Feature extraction, natural language processing (NLP), text analysis, text mining",

author = "Chien-Liang Liu and Hsaio, {Wen Hoar} and Chia-Hoang Lee and Lu, {Gen Chi} and Emery Jou",

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AU - Liu, Chien-Liang

AU - Hsaio, Wen Hoar

AU - Lee, Chia-Hoang

AU - Lu, Gen Chi

AU - Jou, Emery

PY - 2012/5

Y1 - 2012/5

N2 - In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.

AB - In this paper, we design and develop a movie-rating and review-summarization system in a mobile environment. The movie-rating information is based on the sentiment-classification result. The condensed descriptions of movie reviews are generated from the feature-based summarization. We propose a novel approach based on latent semantic analysis (LSA) to identify product features. Furthermore, we find a way to reduce the size of summary based on the product features obtained from LSA. We consider both sentiment-classification accuracy and system response time to design the system. The rating and review-summarization system can be extended to other product-review domains easily.

KW - Feature extraction

KW - natural language processing (NLP)

KW - text analysis

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Movie rating and review summarization in mobile environment

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