A review of approaches for topic detection in Twitter

Mottaghinia, Zeynab; Feizi‐Derakhshi, Mohammad‐Reza; Farzinvash, Leili; Salehpour, Pedram

doi:10.1080/0952813x.2020.1785019

Cited by 20 publications

(23 citation statements)

References 90 publications

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“…To find relevant information or topics is a difficult task since there are millions of daily tweets covering thousands of topics, there is noisy vocabulary (slangs, emoticons, grammar errors), the text is very short (140 characters), and multilingual tweets. Topic detection is a technique for discovering the main topics automatically [13] . Few works explore topic detection for languages different from English, like Portuguese [14] , [15] .…”

Section: Introductionmentioning

confidence: 99%

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Garcia

Berton

2021

Applied Soft Computing

285

148

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Section: Introductionmentioning

confidence: 99%

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Garcia

Berton

2021

Applied Soft Computing

285

148

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“…Topic detection is an automatic technique to discover the topics in a large collection of text data [14]. A topic contains a cluster of words that frequently occur together [15].…”

Section: Topic Detectionmentioning

confidence: 99%

Behavior Analytics, Sentiment Analysis, and Topic Detection of Danmaku from Online Electronics Courses on Bilibili

Zeng¹,

Tan²,

Xia³

et al. 2023

IJIET

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Danmaku data from an online course contains implicit information about the students, the teacher, and the course itself. To discover the information, we design a behavior-sentiment-topic mining procedure, and apply it on the danmaku from two electronics courses on Bilibili, a popular video sharing platform in China. The procedure enables us to obtain behavior patterns, text sentiments, and hidden topics, of those danmaku comments effectively. Results show similarities and differences between the danmaku from Fundamentals of Analog Electronics and that from Fundamentals of Digital Electronics. Some interesting observations are given according to the results. For example, students tend to experience an emotional upsurge right before the end of a course, which is due to their fulfilment for completing the course. Based on the observations, we make some suggestions for students, teachers, and platforms on how to improve the learning outcomes using the results of danmaku analysis.

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“…However, the advanced STTM techniques, data source based, labelling based, word types, and application-based models are not covered in this survey. Mottaghinia et al (2020) explored various techniques to extract and discover topics of tweets. Then, they grouped the topic detection techniques into four classes of categories.…”

Section: Existing Surveysmentioning

confidence: 99%

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

et al. 2022

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Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly embraced by individuals, groups, and organizations as a valuable source of information. This social media generated information comes in the form of tweets or posts, and normally characterized as short text, huge, sparse, and low density. Since many real-world applications need semantic interpretation of such short texts, research in Short Text Topic Modeling (STTM) has recently gained a lot of interest to reveal unique and cohesive latent topics. This article examines the current state of the art in STTM algorithms. It presents a comprehensive survey and taxonomy of STTM algorithms for short text topic modelling. The article also includes a qualitative and quantitative study of the STTM algorithms, as well as analyses of the various strengths and drawbacks of STTM techniques. Moreover, a comparative analysis of the topic quality and performance of representative STTM models is presented. The performance evaluation is conducted on two real-world Twitter datasets: the Real-World Pandemic Twitter (RW-Pand-Twitter) dataset and Real-world Cyberbullying Twitter (RW-CB-Twitter) dataset in terms of several metrics such as topic coherence, purity, NMI, and accuracy. Finally, the open challenges and future research directions in this promising field are discussed to highlight the trends of research in STTM. The work presented in this paper is useful for researchers interested in learning state-of-the-art short text topic modelling and researchers focusing on developing new algorithms for short text topic modelling.

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A review of approaches for topic detection in Twitter

Cited by 20 publications

References 90 publications

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Behavior Analytics, Sentiment Analysis, and Topic Detection of Danmaku from Online Electronics Courses on Bilibili

Short text topic modelling approaches in the context of big data: taxonomy, survey, and analysis

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