Tweets classification and sentiment analysis for personalized tweets recommendation
Satti, Fahad Ahmed
Khan, Wajahat Ali
Khan, Adil Mehmood
AffiliationUniversity of Derby
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AbstractMining social network data and developing user profile from unstructured and informal data are a challenging task. The proposed research builds user profile using Twitter data which is later helpful to provide the user with personalized recommendations. Publicly available tweets are fetched and classified and sentiments expressed in tweets are extracted and normalized. This research uses domain-specific seed list to classify tweets. Semantic and syntactic analysis on tweets is performed to minimize information loss during the process of tweets classification. After precise classification and sentiment analysis, the system builds user interest-based profile by analyzing user’s post on Twitter to know about user interests. The proposed system was tested on a dataset of almost 1 million tweets and was able to classify up to 96% tweets accurately.
CitationKhattak, A.M., Batool, R., Satti, F.A., Hussain, J., Khan, W.A., Khan, A.M. and Hayat, B., (2020). 'Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation. Complexity in Deep Neural Networks, pp. 1-11.
JournalComplexity in Deep Neural Networks
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