CLUSM: An Unsupervised Model for Microblog Sentiment Analysis Incorporating Link Information

Ou, Gaoyan; Chen, Wei; Li, Binyang; Wang, Tengjiao; Yang, Dongqing; Wong, Kam-Fai

doi:10.1007/978-3-319-05810-8_32

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…CRFs, used by 4 studies [228,238,200,199], are a type of discriminative classifier that model the decision boundary amongst different classes, whereas LiR was also used by 4 studies [194,241,232,239]. Moreover, 3 studies each used the SANT [78,75,239] and SGD [227,244,240] algorithms, with the former being mostly used for comparison purposes to the proposed approaches by the respective authors.…”

Section: Algorithm Number Of Studies Referencementioning

confidence: 99%

“…A total of 6 studies [307,242,78,308,177, 81] adopted a probabilistic approach to perform a form of social opinion mining. In particular, [78] propose a novel probabilistic model in the Content and Link Unsupervised Sentiment Model (CLUSM), where the focus is on microblog sentiment classification incorporating link information, namely behaviour, same user and friend.…”

Section: Probabilistic (Pr)mentioning

confidence: 99%

“…Chinese 53 [474,289,150,286,122,415,95,199,546,90,218,57,339,424,496,347,200,106,107,355,312,439,364,139,174,207,14,134,98,135,73,520,166,74,176,510,492,386,389,78,390,493,392,394,189,396,397,470,414,398,501,…”

Section: Language Total Studiesmentioning

confidence: 99%

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Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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Social media popularity and importance is on the increase, due to people using it for various types of social interaction across multiple channels. This social interaction by online users includes submission of feedback, opinions and recommendations about various individuals, entities, topics, and events. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Therefore, through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence, which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, natural language processing and other

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Section: Algorithm Number Of Studies Referencementioning

confidence: 99%

Section: Probabilistic (Pr)mentioning

confidence: 99%

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Over a Decade of Social Opinion Mining: A Systematic Review

Cortis,

Davis

2020

Preprint

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“…Output: Weibos set which user ignore. (1) Any Weibo , ∈ , read the publish time ; (2) Find Weibo , ∈ 3while (the publish time of satisfy ∈ [ − Δ , + Δ ]) (4) ∈ ; // is an intermediate variable. 5While (∀ , ∈ , ∉ ) (6) Add to ; (7) Output ;…”

Section: Feature Descriptionmentioning

confidence: 99%

“…There are a lot of directions on the study of Weibo, including sentiment analysis based on Weibo [1] and Weibo personalized recommendation research [2]. One high practical value direction of the researches in Weibo is studying online behavior of users and corresponding information propagation.…”

Section: Introductionmentioning

confidence: 99%

Weibo Information Propagation Dissemination Based on User Behavior Using ELM

Liu

2015

Mathematical Problems in Engineering

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Information dissemination prediction based on Weibo has been a hot topic in recent years. In order to study this, people always extract features and use machine learning algorithms to do the prediction. But there are some disadvantages. Aiming at these deficiencies, we proposed a new feature, the dependency between the Weibos involved in geographical locations and location of the user. We use ELM to predict behaviors of users. An information dissemination prediction model has also been proposed in this paper. Experimental results show that our proposed new feature is real and effective, and the model we proposed can accurately predict the scale of information dissemination. It also can be seen in the experimental results that the use of ELM significantly reduces the time, and it has a better performance than the traditional method based on SVM.

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Joint Sentiment and Emotion Classification with Integer Linear Programming

Rong

Zhou

et al. 2015

Database Systems for Advanced Applications

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CLUSM: An Unsupervised Model for Microblog Sentiment Analysis Incorporating Link Information

Cited by 7 publications

References 13 publications

Over a Decade of Social Opinion Mining: A Systematic Review

Over a Decade of Social Opinion Mining: A Systematic Review

Weibo Information Propagation Dissemination Based on User Behavior Using ELM

Joint Sentiment and Emotion Classification with Integer Linear Programming

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