Abstract
Performing machine learning and natural language processing task on Twitter data is challenging due to the short and noisy nature of tweets. These tasks perform well on long documents like news articles, research papers but perform poorly when applied on short text like tweets. One way of improving the results is tweet pooling, i.e., to combine the related tweets to make longer coherent input documents. In this work, several new tweet pooling schemes are proposed based on the two tweet auxiliary information—user mentions and URLs. The proposed tweet pooling schemes are evaluated for clustering quality on the clusters/topics obtained by using standard Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). Using the tweet labels of the tweet dataset, purity, and Normalized Mutual Information (NMI) measures are used to evaluate the clustering quality. Empirical results show that proposed tweet pooling schemes always outperform the existing schemes by significant margin when more than one tweet auxiliary information is used for pooling.
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Akhtar, N., Beg, M.M.S. (2021). Improving Microblog Clustering: Tweet Pooling Schemes. In: Sharma, N., Chakrabarti, A., Balas, V., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1174. Springer, Singapore. https://doi.org/10.1007/978-981-15-5616-6_1
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DOI: https://doi.org/10.1007/978-981-15-5616-6_1
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