Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Kuo, Yau‐Hwang; Fu, Meng-Hsuan; Tsai, Wen-Hao; Lee, Kuan‐Rong; Chen, Lingyu

doi:10.1007/s10489-015-0700-z

Cited by 15 publications

(6 citation statements)

References 16 publications

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“…2) Screen emotional active microblog texts through emotional dictionaries. Because microblog users' interest shows positive emotions, microblog texts with negative emotions are removed [26]- [28].…”

Section: Methodsmentioning

confidence: 99%

Building a TIN-LDA Model for Mining Microblog Users’ Interest

Zheng

Wang

2019

IEEE Access

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A latent Dirichlet allocation (LDA) model is a common method for mining the interest of microblog users. But the LDA model does not reflect the hierarchical and dynamic trend of microblog users' interest. As a result, this paper combines with the timeliness and interactivity of microblog, to judge the hierarchical orientation and dynamic interest trend orientation of users' interest. And based on the dynamic interest hierarchical orientation, the three-layers interest network (TIN-LDA) model is constructed to mine the interest of microblog users. In addition, this model expands interest attributes. Interest attributes include contents, contents marked with special symbols, forwarding contents, along with the authentication user name and authentication information. Bringing the interest attributes into users' interest analysis so as to improve the accuracy of mining microblog users' interest keywords and topics. Topic quality assessment and perplexity evaluation were used to verify the effectiveness of the TIN-LDA model in mining the interest of microblog users. INDEX TERMS Dynamic interest hierarchical orientation, LDA topic model, interest topics and keywords, TIN-LDA model, interest attributes.

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Section: Methodsmentioning

confidence: 99%

Building a TIN-LDA Model for Mining Microblog Users’ Interest

Zheng

Wang

2019

IEEE Access

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“…Fan et al [24] analyzed blog text to improve the quality of advertisements in the blogs that were more relevant to the user. To find the blogger's overall emotions towards any particular topic, Kuo et al [25] create a social opinion graph as generally every blogger is somewhat influenced by its social circle. So their social interactions can be used to find the overall sentiment orientation of the blogger.…”

Section: Related Workmentioning

confidence: 99%

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

et al. 2020

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Sentiment analysis is a rapidly growing field of research due to the explosive growth in digital information. In the modern world of artificial intelligence, sentiment analysis is one of the essential tools to extract emotion information from massive data. Sentiment analysis is applied to a variety of user data from customer reviews to social network posts. To the best of our knowledge, there is less work on sentiment analysis based on the categorization of users by demographics. Demographics play an important role in deciding the marketing strategies for different products. In this study, we explore the impact of age and gender in sentiment analysis, as this can help e-commerce retailers to market their products based on specific demographics. The dataset is created by collecting reviews on books from Facebook users by asking them to answer a questionnaire containing questions about their preferences in books, along with their age groups and gender information. Next, the paper analyzes the segmented data for sentiments based on each age group and gender. Finally, sentiment analysis is done using different Machine Learning (ML) approaches including maximum entropy, support vector machine, convolutional neural network, and long short term memory to study the impact of age and gender on user reviews. Experiments have been conducted to identify new insights into the effect of age and gender for sentiment analysis.

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“…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%

“…The Politics domain is the dominant application area with 45 studies applying social opinion mining on different events, namely elections [453,103,197,50,121,87,88,157,53,327,244,539,421,422,337,203,168,368,442,222,520,178,212,184,117,511,190,441,310,406,82], reforms, such as equality marriage [130], debates [180], referendums [241,540], political parties or politicians [60,111,466], and political events, such as terrorism, protests, uprisings and riots [420,437,330,556,205,248,188].…”

Section: Application Areas Of Social Opinion Miningmentioning

confidence: 99%

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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Integrated microblog sentiment analysis from users’ social interaction patterns and textual opinions

Cited by 15 publications

References 16 publications

Building a TIN-LDA Model for Mining Microblog Users’ Interest

Building a TIN-LDA Model for Mining Microblog Users’ Interest

Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning

Over a Decade of Social Opinion Mining: A Systematic Review

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