Real time Sentiment Analysis from Data Streaming

Authors

  • Samit Shivadekar  Department of CSEE, University of Maryland Baltimore County, USA
  • Ketan Shahapure  Department of CSEE, University of Maryland Baltimore County, USA
  • Shivam Vibhute  San Jose State University, San Jose, California, United States
  • Milton Halem  University of Maryland, Baltimore County

DOI:

https://doi.org/10.32628/CSEIT2390646

Keywords:

Twitter, Sentiment Analysis, Real-Time, Streaming Data, Topic Analysis, Model training, Prediction

Abstract

Public sentiment is a potent indicator of how people perceive and receive a topic. It has the power to make or break companies and people. Twitter is one of the best platforms in today’s generation to gauge public sentiment. [10] Utilizing the power and influence Twitter has we decided to create a service that would enable us to know how a trending topic is being viewed by the masses in real-time. The user gives the topic as input to the front-end graphical user interface that topic is then taken and fed to the Twitter streaming API. Tweets containing the hashtag of the topic mentioned by the user are returned and the sentiment of those tweets is predicted and sent to the front end where analysis prediction of the sentiment of the tweets is done dynamically as the tweets come in. By using our service for a few minutes the user will get to know what the overall outlook of a topic is and use that information as a guiding beacon for any future decisions regarding that topic.

References

  1. 'Text Analysis,' MonkeyLearn. https://monkeylearn.com
  2. 'Unlock the hidden emotion with the best sentiment analysis tool,' www.repustate.com. https://www.repustate.com/sentiment-analysis/
  3. 'Sentiment Analysis - Lexalytics,' www.lexalytics.com, May 16, 2022. https://www.lexalytics.com/technology/sentiment-analysis/
  4. G. L. Muller, 'HTML5 WebSocket protocol and its application to distributed computing,' arXiv:1409.3367 [cs], Sep. 2014, Accessed: Dec. 22, 2022. [Online]. Available: https://arxiv.org/abs/1409.3367
  5. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understand-ing,' arXiv.org, Oct. 11, 2018. https://arxiv.org/abs/1810.04805
  6. 'Apache Kafka,' Apache Kafka. https://kafka.apache.org/documentation/
  7. 'Filtered stream introduction,' developer.twitter.com. https://developer.twitter.com/en/docs/twitter-api/tweets/filtered-stream/introduction
  8. 'Welcome to Flask Flask Documentation (2.2.x),' flask.palletsprojects.com. https://flask.palletsprojects.com/en/2.2.x/
  9. C. Richardson, 'Microservices.io,' microservices.io, 2017. https://microservices.io/patterns/microservices.html
  10. Sarlan, Aliza & Nadam, Chayanit & Basri, Shuib. (2014). Twitter sentiment analysis. 212-216. 10.1109/ICIMU.2014.7066632.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2024-02-29

Issue

Volume 10, Issue 1, January-February-2024

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Samit Shivadekar, Ketan Shahapure, Shivam Vibhute, Milton Halem, " Real time Sentiment Analysis from Data Streaming" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.60-70, January-February-2024. Available at doi : https://doi.org/10.32628/CSEIT2390646