Sentiment analysis of online Chinese comments based on statistical learning combining with pattern matching

Tang, Junjian; Hao, Shunli; Qu, Wenqi

doi:10.1002/cpe.4765

Cited by 8 publications

(7 citation statements)

References 4 publications

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“…Basic dictionary is built based on BosonNLP sentiment dictionary [18]. Domain dictionary is built based on SO-PMI (Semantic Orientation Pointwise Mutual Information) [29]. Field dictionary is built based on field classifier [11].…”

Section: The Structure Of Bsetmentioning

confidence: 99%

A Barrage Sentiment Analysis Scheme Based on Expression and Tone

et al. 2019

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Most of existing methods do not consider the influence of expression and tone on barrage sentiment analysis. This decreases the effect and accuracy of barrage sentiment analysis. Therefore, we propose a barrage sentiment analysis scheme based on expression and tone. First, we propose a new sentiment dictionary based on expression and tone to increase the effect of barrage sentiment analysis. Second, we propose a new calculation method of sentiment value based on expression and tone for barrage sentiment analysis to increase the accuracy. Meanwhile, we change a single threshold to a threshold range, to expand the scope of neutral barrages. Finally, the experimental results show that our scheme is more effective and practical than the existing methods in the scenario of barrage sentiment analysis.

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Section: The Structure Of Bsetmentioning

confidence: 99%

A Barrage Sentiment Analysis Scheme Based on Expression and Tone

et al. 2019

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“…(2) We proposed a method of block extraction of opinion targets based on their phrase structures to avoid the situation that the opinion target cannot be extracted completely due to the error of an internal dependency relationship. (3) We proposed a syntactic path between the opinion target and the opinion term to judge whether there is a pointing or modifying relationship between them or not. The seven syntactic path rules we proposed not only cover many direct dependency relationship between the opinion target and the opinion term, but also overcome the situation of indirect dependencies.…”

Section: Of 14mentioning

confidence: 99%

“…In the era of information technology, the Internet and social media have become the prime platforms to share feelings, emotions, and opinions regarding different topics, products, or events in the form of reviews. However, many current studies mainly focus on the classification of polarity of the commentary . We are unable to know the specific targets that the user evaluated.…”

Section: Introductionmentioning

confidence: 99%

CHOpinionMiner: An unsupervised system for Chinese opinion target extraction

Wang

Jiang

et al. 2019

Concurrency and Computation

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Summary Opinion target extraction (OTE) is an important task in fine‐grained sentiment analysis field, which focuses on the identification of the targets of users' opinions or sentiments from online reviews. Existing approaches to OTE are mainly based on unsupervised rule‐based methods or supervised machine learning methods. However, the latter needs a large number of labeled samples to train their models and lack of labeled corpus limits the research progress on OTE of Chinese. In this paper, we proposed a novel unsupervised and domain independent system called CHOpinionMiner. First, noun phrases are extracted as candidate opinion targets based on phrase structure grammar, then the syntactic paths of the candidate opinion targets and the opinion words in one opinionated sentence are extracted. After that, the legal paths, or rather, the opinion targets‐opinion lexicon pairs that are syntactically associated are selected according to the syntactic rules. Finally, not only the formal targets but also the orientations are obtained. Experiments on data sets consisting of microblog topics and product reviews demonstrate that our approach outperforms the existing state‐of‐the‐art methods.

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“…The process shows that the appraisal of events can trigger certain emotional reactions [13] that can affect the behavioral intentions of an individual [14]. To detect the emotions of netizens toward events on social media, researchers constructed various emotional dictionaries [15][16][17][18] and established different machine learning models [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Identifying critical outbreak time window of controversial events based on sentiment analysis

Wang

Zhang

et al. 2020

PLoS ONE

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The response of netizens toward controversial events plays an important guiding role in the development of events. Based on the identification of such responses, this study aimed to determine the critical outbreak time window of events. The microblog texts related to an event were divided into seven emotional categories via multi-emotional analysis to capture the subtle emotions of netizens toward an event, i.e., public opinion. By detecting the characteristics of the text and regional coverage of emotions, an emotional coverage index that reflects the intensity of emotional impact was proposed to determine the mainstream emotion of netizens. By capturing the mutation characteristics of the impact intensity of mainstream emotions, the critical time window of the public opinion toward the event was obtained. The experimental results demonstrated that the proposed method can effectively identify the critical outbreak time window of controversial events, which can help authorities in preventing the further aggravation of events.

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Sentiment analysis of online Chinese comments based on statistical learning combining with pattern matching

Cited by 8 publications

References 4 publications

A Barrage Sentiment Analysis Scheme Based on Expression and Tone

A Barrage Sentiment Analysis Scheme Based on Expression and Tone

CHOpinionMiner: An unsupervised system for Chinese opinion target extraction

Identifying critical outbreak time window of controversial events based on sentiment analysis

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