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Machine Learning Models for Sentiment Analysis of Tweets: Comparisons and Evaluations

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Transforming Management with AI, Big-Data, and IoT

Abstract

Presently, the use of Twitter is increasing, and occurrences of large number of tweets are one of the important sources of personal thoughts and opinions. In social media, sentiment analysis is a significant type for analysis of text to make choice to find out the negative and positive thoughts of the users. In this chapter, we have analysed sentiment analysis of tweets using two machine learning models (Logistic Regression and Decision Tree) to identify the best machine learning algorithms for tweet data analysis. Further, data pre-processing (tokenization and stemming) and data visualization are performed. Data engineering principles are applied to measure the performances and improve the results. Data engineering displays the statistics with different labels, hash tags and word frequency tables. Finally, the performance of both the machine learning algorithms is evaluated using F-1 score. Results demonstrate 4% increase in model performance if Logistic Regression with a particular feature is used.

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Author information

Authors and Affiliations

  1. School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK

    Leeladhar Koti Reddy Vanga & Sukhpal Singh Gill

  2. Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

    Adarsh Kumar

  3. QualiteSoft, Chandigarh, India

    Kamalpreet Kaur

  4. Centre for Climate Change Research, Indian Institute of Tropical Meteorology (IITM), Pune, India

    Manmeet Singh

  5. Interdisciplinary Programme (IDP) in Climate Studies, Indian Institute of Technology (IIT), Bombay, India

    Manmeet Singh

  6. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Vlado Stankovski

Authors
  1. Leeladhar Koti Reddy Vanga
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  2. Adarsh Kumar
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  3. Kamalpreet Kaur
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  4. Manmeet Singh
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  5. Vlado Stankovski
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  6. Sukhpal Singh Gill
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Corresponding author

Correspondence to Sukhpal Singh Gill .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence Engineering, AI and Robotics Institute, Near East University, Mersin, Turkey

    Fadi Al-Turjman

  2. Department of Computer Science and Engineering, G. L. Bajaj Institute of Technology and Management (GLBITM), Affiliated to AKTU, Greater Noida, India

    Satya Prakash Yadav

  3. School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttar Pradesh, India

    Manoj Kumar

  4. Department of Information Technology, Rajkiya Engineering College, Banda, Uttar Pradesh, India

    Vibhash Yadav

  5. Department of Computer Science and Engineering, Faculty of Engineering and Technology, M. S. Ramaiah University of Applied Sciences, Bangalore, Karnataka, India

    Thompson Stephan

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Vanga, L.K.R., Kumar, A., Kaur, K., Singh, M., Stankovski, V., Gill, S.S. (2022). Machine Learning Models for Sentiment Analysis of Tweets: Comparisons and Evaluations. In: Al-Turjman, F., Yadav, S.P., Kumar, M., Yadav, V., Stephan, T. (eds) Transforming Management with AI, Big-Data, and IoT. Springer, Cham. https://doi.org/10.1007/978-3-030-86749-2_16

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  • DOI: https://doi.org/10.1007/978-3-030-86749-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86748-5

  • Online ISBN: 978-3-030-86749-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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