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Can pre-trained Transformers be used in detecting complex sensitive sentences? -- A Monsanto case study ...
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TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing
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DAN: Dual-View Representation Learning for Adapting Stance Classifiers to New Domains ...
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Assessing Social License to Operate from the Public Discourse on Social Media ...
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Recognising Agreement and Disagreement between Stances with Reason Comparing Networks ...
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Catering to your concerns: Automatic generation of personalised security-centric descriptions for Android apps
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In: ACM Transactions on Cyber-Physical Systems, Vol. 3, no. 4 (Sep 2019), article no. 36 (2019)
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Cross-Target Stance Classification with Self-Attention Networks ...
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Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps ...
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Incorporating tweet relationships into topic derivation
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Abstract:
With its rapid users growth, Twitter has become an essential source of information about what events are happening in the world. It is critical to have the ability to derive the topics from Twitter messages (tweets), that is, to determine and characterize the main topics of the Twitter messages (tweets). However, tweets are very short in nature and therefore the frequency of term co-occurrences is very low. The sparsity in the relationship between tweets and terms leads to a poor characterization of the topics when only the content of the tweets is used. In this paper, we exploit the relationships between tweets and propose intLDA, a variant of Latent Dirichlet Allocation (LDA) that goes beyond content and directly incorporates the relationship between tweets. We have conducted experiments on a Twitter dataset and evaluated the performance in terms of both topic coherence and tweet-topic accuracy. Our experiments show that intLDA outperforms methods that do not use relationship information. ; 14 page(s)
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Keyword:
topic derivation; tweets relationship; twitter
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URL: http://hdl.handle.net/1959.14/1058264
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