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Comparing Twitter Data for Topic Modling, Clustering, and Predictive Analysis Using LSTM Model

In: City, Society, and Digital Transformation

Author

Listed:
  • Md. Shamaun Islam

    (Chongqing University of PT)

  • Sadat Bin Shahid

    (Hubei University of Technology)

Abstract

Extraction topics to find the difference between the two datasets of information, as a rule, seems unused. Machine learning and the calculation of Natural language Process correction are used to analyze the ever-changing statistics of Twitter accessible online, including modelling processes for a longer time. Two datasets of content information were selected to evaluate comparisons based on specific statistical tests, such as quality and response, accuracy, and statistical tests for particular measurements, such as Portion F and Title. Twitter has become amongst the most often used online network site because anyone can easily publish information about their thoughts on a specific issue via a public message called a tweet. Twitter play an essential part in the prioritization of public life. It is necessary to know about the topic and domain on social media sites. This research work performs a topic modelling on Twitter data related to covid19. Two different data sets are discussed, and tweets are clustered through the k-mean clustering algorithm. Topics are also found in each cluster using the LDA technique. The clustered data sets are predictively analyzed through the LSTM model. The results show that the model achieves 96% accuracy.

Suggested Citation

  • Md. Shamaun Islam & Sadat Bin Shahid, 2022. "Comparing Twitter Data for Topic Modling, Clustering, and Predictive Analysis Using LSTM Model," Lecture Notes in Operations Research, in: Robin Qiu & Wai Kin Victor Chan & Weiwei Chen & Youakim Badr & Canrong Zhang (ed.), City, Society, and Digital Transformation, chapter 0, pages 375-392, Springer.
    • Handle: RePEc:spr:lnopch:978-3-031-15644-1_28
    DOI: 10.1007/978-3-031-15644-1_28
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