Title

Tweets classification and sentiment analysis for personalized tweets recommendation

Source of Publication

Complexity

Abstract

© 2020 Asad Masood Khattak et al. 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.

Document Type

Article

Publisher

Hindawi Limited

Publication Date

1-1-2020

DOI

10.1155/2020/8892552

Scopus ID

85098558325

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