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

Peng, Ting-Chun; Shih, Chia-Chun

doi:10.1109/icdim.2010.5664631

Cited by 17 publications

(7 citation statements)

References 12 publications

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“…In terms of opinion words, intransitive stative verbs are the most frequent ones appearing in the gathered reviews that are similar part of speech patterns in Peng and Shih's finding (Peng & Shih, 2010). Finally, 454 opinion words are adopted after text preprocessing and the features selection process.…”

Section: Resultsmentioning

confidence: 99%

Opinion mining of hotel customer-generated contents in Chinese weblogs

Chiu

Sung

et al. 2013

Current Issues in Tourism

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Customer-generated contents in weblogs provide tourism organisations with valuable market intelligence and ongoing market research opportunities. In this study, an opinion mining method based on feature-based sentiment classification is proposed to extract the online electronic word-of-mouth on weblogs in Taiwan. For opinion extraction, a supervised semantic orientation using the point-wise mutual information (SO_PMI) algorithm based on the extension of Turney's unsupervised SO_PMI algorithm is proposed to extract the opinion words. In addition, a heuristic n-phrase rule is proposed to find out customer opinions about hotel attributes, including hotel image, services, price/value, food and beverage, room, amenities, and location. The experimental results show that the proposed approach mixed with supervised SO_PMI algorithm and heuristic n-phrase rule can demonstrate its effectiveness with acceptable classification and forecasting performances. Furthermore, a perceptual map based on correspondence analysis visually presents opinions comparison to provide the insight of competitive positions.

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Section: Resultsmentioning

confidence: 99%

Opinion mining of hotel customer-generated contents in Chinese weblogs

Chiu

Sung

et al. 2013

Current Issues in Tourism

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“…This approach is not limited to any product, so it has wide applicability. In reference [4], the authors observed Chinese structure and finished in a combination of different parts of speech. They proposed a set of part-of-speech (POS) patterns that may locate opinion words with high accuracy.…”

Section: A Backgroundmentioning

confidence: 98%

“…Reference [4] proposed an opinion extraction method using part of speech pattern to find the location of opinion words. Since CKIP POS tag is streamline POS, and their POS is a simplified mark, we converted to streamline POS tags and recombination.…”

Section: Establish Opinion Dictionarymentioning

confidence: 99%

SVR-based outlier detection and its application to hotel ranking

Hsieh

Klyuev

Zhao

et al. 2014

2014 IEEE 6th International Conference on Awareness Science and Technology (iCAST)

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With the rapid advance in information technology, more and more information exchange platforms appear. People can freely exchange information on these platforms. However, not all information is reliable. To make correct decisions, it is necessary to detect and remove unreliable information. The main purpose of this study is to improve the reliability of hotel ranking by detecting and deleting outlier on-line reviews. For this purpose, we design a support vector regression (SVR) based outlier detector using existing on-line reviews. Intuitively, normal reviews are regular, and can be correctly labeled by the SVR detector. Outlier reviews, on the other hand, are usually not regular, and cannot be correctly labeled. Thus, a well-designed SVR-detector can help us to delete outlier reviews automatically. Results obtained in this study are useful not only for hotel ranking. In principle it can be good for recommendation of any services.

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“…However, word segmentation remains a time-consuming and context-dependent task. Besides above issues, preprocessing steps such as stop-words removal, stemming and synonym matching are also frequently required in those word-based methods. Some techniques even need to analyze the POS (Part of Speech) of each word, which increases the cost and the complexity of the sentiment identification (Peng and Shih, 2010). As the advances in social media platforms, people are no longer satisfied with receiving information passively, and they are also willing to express their own opinions and emotions through online.…”

Section: Related Workmentioning

confidence: 99%

Characters-based sentiment identification method for short and informal Chinese text

Lan

2018

IDD

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Purpose Sentiment identification of Chinese text faces many challenges, such as requiring complex preprocessing steps, preparing various word dictionaries carefully and dealing with a lot of informal expressions, which lead to high computational complexity. Design/methodology/approach A method based on Chinese characters instead of words is proposed. This method represents the text into a fixed length vector and introduces the chi-square statistic to measure the categorical sentiment score of a Chinese character. Based on these, the sentiment identification could be accomplished through four main steps. Findings Experiments on corpus with various themes indicate that the performance of proposed method is a little bit worse than existing Chinese words-based methods on most texts, but with improved performance on short and informal texts. Especially, the computation complexity of the proposed method is far better than words-based methods. Originality/value The proposed method exploits the property of Chinese characters being a linguistic unit with semantic information. Contrasting to word-based methods, the computational efficiency of this method is significantly improved at slight loss of accuracy. It is more sententious and cuts off the problems resulted from preparing predefined dictionaries and various data preprocessing.

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

Cited by 17 publications

References 12 publications

Opinion mining of hotel customer-generated contents in Chinese weblogs

Opinion mining of hotel customer-generated contents in Chinese weblogs

SVR-based outlier detection and its application to hotel ranking

Characters-based sentiment identification method for short and informal Chinese text

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