A BERT Framework to Sentiment Analysis of Tweets

Bello, Abayomi, Ng, Sin-Chun and Leung, Man Fai ORCID logoORCID: https://orcid.org/0000-0002-7753-0136 (2023) A BERT Framework to Sentiment Analysis of Tweets. Sensors, 23 (1). ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s23010506


Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

Item Type: Journal Article
Keywords: sentiment analysis, deep learning, tweets, BERT, LSTM, CNN
Faculty: Faculty of Science & Engineering
SWORD Depositor: Symplectic User
Depositing User: Symplectic User
Date Deposited: 13 Jan 2023 13:18
Last Modified: 13 Jan 2023 13:18
URI: https://arro.anglia.ac.uk/id/eprint/708178

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