Robust Word-Network Topic Model for Short Texts

Wang, Fei; Li, Rui; Zuo, Yuan; Zhang, Hui; Zhang, He; Wu, Junjie

doi:10.1109/ictai.2016.0132

Cited by 4 publications

(11 citation statements)

References 13 publications

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“…The time complexity of the WNTM algorithm is O KNIc(c − 1 , where K is the number of pre-defined hidden topics, N denotes number of documents in the dataset, c is size of sliding window, I is the average length of every document in D . Wang et al (2016) developed the Robust WNTM (R-WNTM) as an extension for Short Texts. As the irrelevant data in the word-word space building procedure of WNTM is high, the R-WNTM that filters the unrelated data during the sampling process is presented.…”

Section: Global Word Co-occurrences Based Asttm Modelsmentioning

confidence: 99%

“…They chose nine topic-related groups and sampled 182,671 tweets in the total under those categories. This Tweets dataset has been utilized in a few studies (Jiang et al 2018;Wang et al 2016;Zuo, Wu, et al 2016a;Lin et al 2020a;Indra and Pulungan 2019). Table 11 describes the details of this dataset.…”

Section: Tweets Datasetmentioning

confidence: 99%

“…The description of each news in the dataset is retained as standard short text, and its content is one or two simple sentences of this news article. This dataset is used for many STTM models, such as those (Jiang et al 2018;Wang et al 2016;Zuo, Wu, et al 2016a;and (Lin et al 2020a), whereas the other models (Nguyen et al 2015;and Yi et al 2020) used 32,600 English news and the detail is introduced in Table 11.…”

Section: News Datasetmentioning

confidence: 99%

“…Collected from Weibo CHI (Ni et al 2018;Yuan Zuo, Zhao, et al 2016b;Liqing et al 2019;Li et al 2013;Zheng et al 2019) Specifically TweetSet EN (Yin and Wang 2014;Wu and Li 2019;Qiang et al 2018b;and Mazarura et al 2020) Publically Tweets EN (Jiang et al 2018;Wang et al 2016;Zuo, Wu, et al 2016a ;Lin et al 2020a;Zuo et al 2021;Chen et al 2020b;and Liqing et al 2019) Publically Tweets2011 EN (Cheng et al 2014;Liang et al 2018;Li et al 2019b;Sharath et al 2019;Rashid et al 2019b;Yan et al 201...…”

Section: Specificallymentioning

confidence: 99%

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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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Section: Global Word Co-occurrences Based Asttm Modelsmentioning

confidence: 99%

Section: Tweets Datasetmentioning

confidence: 99%

Section: News Datasetmentioning

confidence: 99%

Section: Specificallymentioning

confidence: 99%

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

et al. 2022

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“…In addition, WNTM contains a huge data that is not relevant in word-word space. An extension to WNTM, Wang et al [57] introduced a novel model called Robust WNTM (R-WNTM), which filters the unrelated data during the sampling process is presented as the irrelevant data in the word-word space building procedure of WNTM is high, Jiang et al [58] suggested WNTM with Word2Vector (WNTM-W2V) to discover deep meaning among words to increase the accuracy of relationship among words as well as to improve topic coherence. Wu et al [59] introduced a clustering method for short texts based on the (BG & SLF-Kmeans) method.…”

Section: Short Text Topic Modellingmentioning

confidence: 99%

Enhancing Big Social Media Data Quality for Use in Short-Text Topic Modeling

et al. 2022

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With the emergence of microblogging platforms and social media applications, large amounts of user-generated data in the form of comments, reviews, and brief text messages are produced every day. Microblog data is typically of poor quality; hence improving the quality of the data is a significant scientific and practical challenge. In spite of the relevance of the problem, there has been not much work so far, especially in regard to microblog data quality for Short-Text Topic Modelling (STTM) purposes. This paper addresses this problem and proposes an approach called the social media data cleansing model (SMDCM) to improve data quality for STTM. We evaluate SMDCM using six topic modelling methods, namely the Latent Dirichlet Allocation (LDA), Word-Network Topic Model (WNTM), Pseudo-document-based Topic Modelling (PTM), Biterm Topic Model (BTM), Global and Local word embedding-based Topic Modeling (GLTM), and Fuzzy Topic modelling (FTM). We used the Real-world Cyberbullying Twitter (RW-CB-Twitter) and the Cyberbullying Mendeley (CB-MNDLY) datasets in the evaluation. The results proved the efficiency of the GLTM and WNTM over the other STTM models when applying the SMDCM techniques, which achieved optimum topic coherence and high accuracy values on RW-CB-Twitter and CB-MNDLY datasets.

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An integrated probabilistic graphic model and FMEA approach to identify product defects from social media data

2021

Expert Systems with Applications

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Robust Word-Network Topic Model for Short Texts

Cited by 4 publications

References 13 publications

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

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

Enhancing Big Social Media Data Quality for Use in Short-Text Topic Modeling

An integrated probabilistic graphic model and FMEA approach to identify product defects from social media data

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