Fuzzy topic modeling approach for text mining over short text |
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| Institution: | 1. Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran;2. Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran;1. Faculty of Informatics, Università della Svizzera italiana (USI), Lugano, Switzerland;2. NLP & IR Group, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain;1. School of Computer and Information Technology & Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China;2. Division of Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA;1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China;2. School of Computer Information Engineering, Jiangxi Normal University, Nanchang, China;3. College of Business, Stony Brook University, USA;1. College of Computer Science and Technology, Jilin University, China;2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, China;3. School Of Software Engineering, South China University of Technology, China |
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| Abstract: | In this era, the proliferating role of social media in our lives has popularized the posting of the short text. The short texts contain limited context with unique characteristics which makes them difficult to handle. Every day billions of short texts are produced in the form of tags, keywords, tweets, phone messages, messenger conversations social network posts, etc. The analysis of these short texts is imperative in the field of text mining and content analysis. The extraction of precise topics from large-scale short text documents is a critical and challenging task. The conventional approaches fail to obtain word co-occurrence patterns in topics due to the sparsity problem in short texts, such as text over the web, social media like Twitter, and news headlines. Therefore, in this paper, the sparsity problem is ameliorated by presenting a novel fuzzy topic modeling (FTM) approach for short text through fuzzy perspective. In this research, the local and global term frequencies are computed through a bag-of-words (BOW) model. To remove the negative impact of high dimensionality on the global term weighting, the principal component analysis is adopted; thereafter the fuzzy c-means algorithm is employed to retrieve the semantically relevant topics from the documents. The experiments are conducted over the three real-world short text datasets: the snippets dataset is in the category of small dataset whereas the other two datasets, Twitter and questions, are the bigger datasets. Experimental results show that the proposed approach discovered the topics more precisely and performed better as compared to other state-of-the-art baseline topic models such as GLTM, CSTM, LTM, LDA, Mix-gram, BTM, SATM, and DREx+LDA. The performance of FTM is also demonstrated in classification, clustering, topic coherence and execution time. FTM classification accuracy is 0.95, 0.94, 0.91, 0.89 and 0.87 on snippets dataset with 50, 75, 100, 125 and 200 number of topics. The classification accuracy of FTM on questions dataset is 0.73, 0.74, 0.70, 0.68 and 0.78 with 50, 75, 100, 125 and 200 number of topics. The classification accuracies of FTM on snippets and questions datasets are higher than state-of-the-art baseline topic models. |
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