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Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams

E. Susi*, A. P. Shanthi

Department of Computer Science & Engineering, Anna University, Chennai, 600025, Tamil Nadu, India

* Corresponding Author: E. Susi. Email: email

Computer Systems Science and Engineering 2023, 45(3), 3231-3246. https://doi.org/10.32604/csse.2023.032104

Abstract

Handling sentiment drifts in real time twitter data streams are a challenging task while performing sentiment classifications, because of the changes that occur in the sentiments of twitter users, with respect to time. The growing volume of tweets with sentiment drifts has led to the need for devising an adaptive approach to detect and handle this drift in real time. This work proposes an adaptive learning algorithm-based framework, Twitter Sentiment Drift Analysis-Bidirectional Encoder Representations from Transformers (TSDA-BERT), which introduces a sentiment drift measure to detect drifts and a domain impact score to adaptively retrain the classification model with domain relevant data in real time. The framework also works on static data by converting them to data streams using the Kafka tool. The experiments conducted on real time and simulated tweets of sports, health care and financial topics show that the proposed system is able to detect sentiment drifts and maintain the performance of the classification model, with accuracies of 91%, 87% and 90%, respectively. Though the results have been provided only for a few topics, as a proof of concept, this framework can be applied to detect sentiment drifts and perform sentiment classification on real time data streams of any topic.

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APA Style
Susi, E., Shanthi, A.P. (2023). Sentiment drift detection and analysis in real time twitter data streams. Computer Systems Science and Engineering, 45(3), 3231-3246. https://doi.org/10.32604/csse.2023.032104
Vancouver Style
Susi E, Shanthi AP. Sentiment drift detection and analysis in real time twitter data streams. Comput Syst Sci Eng. 2023;45(3):3231-3246 https://doi.org/10.32604/csse.2023.032104
IEEE Style
E. Susi and A.P. Shanthi, "Sentiment Drift Detection and Analysis in Real Time Twitter Data Streams," Comput. Syst. Sci. Eng., vol. 45, no. 3, pp. 3231-3246. 2023. https://doi.org/10.32604/csse.2023.032104



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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