Topical affinity in short text microblogs

Wandabwa, Herman Masindano; Naeem, M. Asif; Mirza, Farhaan; Pears, Russel

doi:10.1016/j.is.2020.101662

Cited by 8 publications

(6 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 11 shows the details of these datasets. (Korshunova et al 2019;Belford et al 2016;Kumar and Vardhan 2019;Ni et al 2018;Yan et al 2012;Fang et al 2017;Fang et al 2014;Muliawati and Murfi 2017;Prakoso et al 2018;Pornwattanavichai et al 2020;Capdevila et al 2017;Mustakim et al 2019;Zhang and Zhang 2020;Ali and Balakrishnan 2021;Abdulwahab et al 2022;Yu et al 2017;Indra and Pulungan 2019;Curiskis et al 2020;He et al 2020b;Fang et al 2017;Karami et al 2018Wandabwa et al 2021…”

Section: Web Snippets Datasetmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Web Snippets Datasetmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In the process of automatic extraction of web page text information under the degree of network topology coincidence, in order to ensure the accuracy of extraction, it is necessary to extract the relevant features of web page text information, and the features of web page text information usually consist of multiple dimensions, which will make the process of web page text information extraction too complex, thus reducing the accuracy of web page text information extraction [16,17]. Because the dimension of web page text information contains a large amount of redundant data, it is necessary to reduce the dimension of web page text information, retain the main feature information, and eliminate the impact of redundant data [18]. The specific method is as follows.…”

Section: Dimensionality Reduction Of Web Page Textmentioning

confidence: 99%

Automatic Extraction of Web Page Text Information Based on Network Topology Coincidence Degree

Shu

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In order to effectively solve the above problems, an automatic extraction method of web text information based on network topology coincidence degree is proposed. Search engine, web crawler, and hypertext tag are used to classify web text information, and then, dimensionality reduction is carried out. After processing, the similarity of different features of web page text information is calculated, the similarity is sorted, and the similar text information is extracted according to the correlation based on segment estimation. The experimental results show that the designed method can simplify the complexity of the associated information of the data set and improve the amount of data collection and the success rate of information collection.

show abstract

“…Through analysis of the data over the past three years, they put forward some suggestions on attracting users in the COVID-19 crisis. At present, there are many methods that have been proposed for "network data mining", such as latent Dirichlet allocation (LDA) model [4,5], long short-term memory (LSTM) [6,7], Biterm method for short text distance measurement (BDM) [8,9], targeted aspects oriented topic modeling (TATM) [10], swarm intelligence (SI) algorithms [11], natural language processing (NLP) [12][13][14][15], etc., but they all have their advantages and disadvantages [16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…Improving the research of specific text data mining based on Chinese to use the network text knowledge database effectively is also the focus of this field. The emotion analysis is another part of data mining [5,6,8,[16][17][18][19][20]. How to use scientific methods to mine and match the emotional tendency of short text data is the crucial point.…”

Section: Introductionmentioning

confidence: 99%

“…Suganya et al compared the performance of PSO, bat, GWO, and other algorithms, and found the algorithm suitable for finding the optimal solution based on the data set of BBC sports news [11]. Herman et al used a Twitter data set to verify whether their method of calculating user interest is effective (DOI) [19]. The number of data mining experiments for Chinese text has been gradually increasing in recent years.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Xiong

Yan

Yao³

et al. 2022

Information

View full text Add to dashboard Cite

In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and quantify the emotional orientation matching. Based on the obtained data, we design a visual interaction logic and usability test. These prove that the proposed method is feasible and effective.

show abstract

Topical affinity in short text microblogs

Cited by 8 publications

References 22 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

Automatic Extraction of Web Page Text Information Based on Network Topology Coincidence Degree

Design Demand Trend Acquisition Method Based on Short Text Mining of User Comments in Shopping Websites

Contact Info

Product

Resources

About