Sentiment Classification based on Linguistic Patterns in Citation Context

Wang, Mingyang; Leng, Dongtian; Ren, Jinjin; Zeng, Yiming; Chen, Guangsheng

doi:10.18520/cs/v117/i4/606-616

Cited by 9 publications

(6 citation statements)

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“…• Assumes independence of features • Prediction accuracy is lower than that of other models SVM (M. Wang et al, 2019) • Works well with a clear margin of separation • Effective in high dimensional spaces…”

Section: Table 2 Techniques Used For Citation Content and Context Ana...mentioning

confidence: 99%

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

et al. 2021

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Section: Table 2 Techniques Used For Citation Content and Context Ana...mentioning

confidence: 99%

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

et al. 2021

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“…Citation information is a valuable resource in scientific literature research [9][10][11]. It has been shown that the citation context provides valuable evidence that can be used in modeling the authors' research interests, as well as in many Natural Language Processing (NLP) applications such as publication summarization [5], survey article generation [12], sentiment analysis [13], author co-citation analysis [14], and recommendation system [15], among others.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Retaining these tags will interfere with the extraction of the citation summary. In our previous work on identifying citation emotions, we preprocessed the dataset in detail [13]. The citation fragments without polarity tags and the cited sentences irrelevant to the citation content were deleted.…”

Section: Datamentioning

confidence: 99%

Generating a Citation Summary Based on Cited Sentences and the Implied Citation Emotions

et al. 2021

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This paper aims to generate an emotional citation summary for articles on the basis of the cited sentences and their accompanying emotions. The cited sentences to the same article were divided into three corpora according to their positive, negative or neutral emotion. Summaries were extracted from the three corpora respectively, and were then combined into a comprehensive emotional citation summary for the article. In the summary generation process, the BERT pre-training model was used to vectorize the cited sentences, Fuzzy c-means was used to cluster the vectorized citing sentences, and a LexRank + MMR combination model was applied to extract summary sentences from the clusters. The experimental results show that our algorithm outperforms all the baseline algorithms in generating citation summaries. Based on the content of the summary, the emotional citation summary was found comprehensively to feedback academic peers' evaluation of the article, in such a way as to more comprehensively describe the function and contribution of the article in the scientific community.

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“…To detect the response of netizens toward events, researchers have constructed different emotional dictionaries for different social media platforms [15][16][17][18]. Various techniques of machine learning [19][20][21][22][23][24], deep learning [38][39][40], and natural language processing [41,42] are also widely used in the emotional analysis of eventrelated texts [43][44][45][46].…”

Section: Competing Interests: Sz Was Affiliated With Asiamentioning

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 Classification based on Linguistic Patterns in Citation Context

Cited by 9 publications

References 0 publications

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

A decade of in-text citation analysis based on natural language processing and machine learning techniques: an overview of empirical studies

Generating a Citation Summary Based on Cited Sentences and the Implied Citation Emotions

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

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