Mining Chinese Reviews

Shi, Bin; Chang, Kuiyu

doi:10.1109/icdmw.2006.110

Cited by 16 publications

(5 citation statements)

References 5 publications

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“…However, only few works have been done for Chinese [8,13,16,19]. Most of previous studies for sentiment mining can be divided into two approaches: machine learning approach and statistical approach.…”

Section: Related Workmentioning

confidence: 99%

Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews

Peng

Shih

2010

2010 Fifth International Conference on Digital Information Management (ICDIM)

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Accelerated growth of the Internet has enabled users worldwide to share their feelings and experiences. User-generated content (UGC) websites are the most abundant sources of user reviews. Accurately identifying sentiment phrases is essential to understand the expressed opinions in user reviews. To achieve this, part-of-speech (POS) patterns of phrases are useful. However, previous studies for Chinese opinion extraction only translate English POS patterns directly into Chinese for this task without considering the feasibility. Therefore, this work proposes a Chinese opinion extraction method that exploits the observed Sinica Treebank POS patterns for sentiment phrase identification. Sinica Treebank is a widely representative POS corpus for Chinese. The results of preliminary experiments indicate that the proposed method is highly effective in extracting opinions from Chinese UGC reviews.

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Section: Related Workmentioning

confidence: 99%

Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews

Peng

Shih

2010

2010 Fifth International Conference on Digital Information Management (ICDIM)

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“…As a burgeoning research topic, review mining has already attracted a comprehensive set of theories and technologies. For example, Shi and Chang (2006) extracted product feature-orientation (sentiment) pairs from online product reviews, and Ding et al (2008) determined the semantic orientations (positive, negative, or neutral) of opinions expressed on product features in reviews using a holistic lexicon-based approach. Chaovalit and Zhou (2005) compared supervised and unsupervised classification approaches to mine movie reviews, while Zhuang et al (2006) integrated WordNet, statistical analysis, and movie knowledge to determine whether opinions were positive or negative.…”

Section: Online Review Miningmentioning

confidence: 99%

Measuring book impact based on the multi-granularity online review mining

et al. 2016

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As with articles and journals, the customary methods for measuring books' academic impact mainly involve citations, which is easy but limited to interrogating traditional citation databases and scholarly book reviews. Researchers have attempted to use other metrics, such as Google Books, libcitation, and publisher prestige. However, these approaches lack content-level information and cannot determine the citation intentions of users. Meanwhile, the abundant online review resources concerning academic books can be used to mine deeper information and content utilizing altmetric perspectives. In this study, we measure the impacts of academic books by multi-granularity mining online reviews, and we identify factors that affect a book's impact. First, online reviews of a sample of academic books on Amazon.cn are crawled and processed. Then, multi-granularity review mining is conducted to identify review sentiment polarities and aspects' sentiment values. Lastly, the numbers of positive reviews and negative reviews, aspect sentiment values, star values, and information regarding helpfulness are integrated via the entropy method, and lead to the calculation of the final book impact scores. The results of a correlation analysis of book impact scores obtained via our method versus traditional book citations show that, although there are substantial differences between subject areas, online book reviews tend to reflect the academic impact. Thus, we infer that online reviews represent a promising source for mining book impact within the altmetric perspective and at the multi-granularity content level. Moreover, our proposed method might also be a means by which to measure other books besides academic publications.

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“…Now the study of mining product aspects in the Chinese reviews still at an initial stage. Shi et al [10] extracted product aspects from Chinese reviews, but they need to manually create a conceptual model based on product attributes. Yu [11] proposed a model that integrated natural language processing (NLP) technology with support vector machine, and the average F1score is 87.3%.…”

Section: Introductionmentioning

confidence: 99%

Research on Technology Oriented Framework of Aspects Extraction from Customer Reviews

Shi

2014

AMM

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As e-business develops rapidly, more and more product information and product reviews are posted on the Internet. These contents will have a great significance for companies and consumers. This paper focus on customer reviews of product, and construct a technology oriented research framework for the sentiment analysis. Further more an improved theoretical framework of aspects extraction is proposed, which based on products feature mining issues from customer reviews. This two theoretical framework can help researchers acquire supported valuable data for additional researches including the study of behavioral.

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Mining Chinese Reviews

Cited by 16 publications

References 5 publications

Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews

Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews

Measuring book impact based on the multi-granularity online review mining

Research on Technology Oriented Framework of Aspects Extraction from Customer Reviews

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