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Keyword extraction from Tweets using NLP tools for collecting relevant news

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dc.contributor.author Jayasiriwardene, Thiruni D.
dc.contributor.author Ganegoda, Gamage Upeksha
dc.date.accessioned 2021-07-05T16:56:44Z
dc.date.available 2021-07-05T16:56:44Z
dc.date.issued 2020
dc.identifier.citation Jayasiriwardene, Thiruni D., Ganegoda, Gamage Upeksha (2020). Keyword extraction from Tweets using NLP tools for collecting relevant news. In : International Research Conference on Smart Computing and Systems Engineering, 2020. Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, p.129. en_US
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/23085
dc.description.abstract Keywords play a major role in representing the gist of a document. Therefore, a lot of Natural Language processing tools have been implemented to identify keywords in both structured and unstructured texts. Text that appears in social media platforms such as twitter is mostly unstructured because of the character limitation. Consequently, a lot of short terms and symbols such as emoticons and URLs are included in tweets. Keyword extraction from grammatically ambiguous text is not easy compared to structured text since it is hard to rely on the linguistic features in unstructured texts. But when it comes to news on twitter, it may contain somewhat structured text than informal text does but it depends on the tweeter, the person who posts the tweet. In this paper, a methodology is proposed to extract keywords from a given tweet to retrieve relevant news that has been posted on twitter, for fake news detection. The intention of extracting keywords is to find more related news efficiently and effectively. For this approach, a corpus that contains tweet texts from different domains is built in order to make this approach more generic instead of making it a domainspecific approach. In fact, the Stanford Core NLP tool kit, Wordnet linguistic database and statistical method are used for extracting keywords from a tweet. For the system evaluation, the Turing test which has human intervention is used. The system was able to acquire an accuracy of 67.6% according to the evaluation conducted. en_US
dc.publisher Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka en_US
dc.subject Named Entity Recognition (NER), Natural Language Processing (NLP), Part of speech tagging, Stanford Core NLP, Wordnet corpus. en_US
dc.title Keyword extraction from Tweets using NLP tools for collecting relevant news en_US


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