A Framework for Real-time Sentiment Analysis of Big Data Generated by Social Media Platforms

Fahd, Kiran; Parvin, Salma; Souza-Daw, Anthony de

doi:10.1109/itnac53136.2021.9652148

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…In addition, in the era of information abundance, there is an unprecedented growth in the demand for online public opinion expression, leading to a large amount of information received and transmitted every day or many tweets posted on the Internet or the spread of misinformation. Some of the information may threaten public safety, which is a great challenge to both Internet-related administrations and public and social stability (Jain et al, 2021;Fahd et al,2021;Deniz et al, 2021). Hence, analyzing the content posted by users on Weibo and actively guiding public opinion to reduce the impact of misinformation are necessary means to maintain societal stability and security (Liu, 2020;Wang & Shin, 2019).…”

Section: Introductionmentioning

confidence: 99%

Sentiment Classification of Social Network Text Based on AT-BiLSTM Model in a Big Data Environment

Liu

2023

International Journal of Information Technologies and Systems Approach

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To tackle the challenge of ineffective sentiment prediction using current sentiment classification methods, this paper introduces a method social network text sentiment classification. The method leverages a bidirectional short and long-term memory model (AT-BiLSTM), specifically designed for a big data environment. First, a vectorized representation of text is realized by introducing a pre-trained BERT model, and the classification results are dynamically adjusted according to the semantic information of the words. Then, the BiLSTM combined with the attention mechanism performs aspect-level sentiment analysis, and the corresponding model AT-BiLSTM is formulated. Finally, the BERT model randomly selects input tags for information masking and pre-trains the proposed model. The proposed method was evaluated against three alternative methods using an identical dataset. The results show that the novel method achieved the highest accuracy, recall, and F1-score, reaching 93.72%, 93.91%, and 92.38%, respectively. Consequently, the proposed method demonstrates superior performance compared to the other three methods evaluated.

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