Tweets Classification and Sentiment Analysis for Personalized Tweets Recommendation

Mining 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 clas...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2020, Vol.2020 (2020), p.1-11
Main Authors: Hayat, Bashir, Khan, Wajahat Ali, Hussain, Jamil, Satti, Fahad Ahmed, Batool, Rabia, Khattak, Asad Masood, Khan, Adil Mehmood
Format: Article
Language:English
Subjects:
Accuracy
Automation
Classification
Community support
Customization
Data mining
Keywords
Propagation
Semantics
Sentiment analysis
Social networks
Unstructured data
User behavior
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Summary:Mining 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.
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/8892552