Tweets classification and sentiment analysis for personalized tweets recommendation
Journal article
Authors | Batool, Rabia, Satti, Fahad Ahmed, Hussain, Jamil, Khan, Wajahat Ali, Khan, Adil Mehmood and Hayat, Bashir |
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Abstract | 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. |
Keywords | data mining; social network; Twitter |
Year | 2020 |
Journal | Complexity in Deep Neural Networks |
Complexity | |
Journal citation | 2020 |
Publisher | Hindawi |
ISSN | 10762787 |
10990526 | |
Digital Object Identifier (DOI) | https://doi.org/10.1155/2020/8892552 |
Web address (URL) | http://hdl.handle.net/10545/625591 |
http://creativecommons.org/licenses/by/4.0/ | |
hdl:10545/625591 | |
Publication dates | 17 Dec 2020 |
Publication process dates | |
Deposited | 05 Feb 2021, 15:07 |
Accepted | 01 Dec 2020 |
Rights | Attribution 4.0 International |
Contributors | University of Derby |
File | File Access Level Open |
File | File Access Level Open |
https://repository.derby.ac.uk/item/92v4w/tweets-classification-and-sentiment-analysis-for-personalized-tweets-recommendation
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