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Multi stage sentiment analysis for product reviews on Twitter using optimized machine learning algorithm.

Lakshmi Prasad Mudarakola1, Ranjith Kumar Gatla2, Akella S Narasimha Raju3

  • 1Department of Computer Science and Engineering, Institute of Aeronautical Engineering, Hyderabad, Telangana, 500043, India.

Scientific Reports
|November 13, 2025
PubMed
Summary

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This summary is machine-generated.

This study demonstrates machine learning

Area of Science:

  • Computational Linguistics
  • Social Media Analytics
  • Marketing Science

Background:

  • Consumer feedback is increasingly found on social media platforms like Twitter.
  • Analyzing this feedback provides valuable insights for product development and marketing.
  • Traditional methods may not fully capture the nuances of social media sentiment.

Purpose of the Study:

  • To explore the feasibility of using machine learning for sentiment classification of product-related tweets.
  • To compare the effectiveness of conventional and deep learning models for this task.
  • To identify the optimal sentiment classification framework for social media product discussions.

Main Methods:

  • A multi-stage framework combining Support Vector Machines (SVM), Naive Bayes, Random Forest, and Long Short-Term Memory (LSTM) networks.
Keywords:
Customer reactionsCustomer sentimentsMachine learningPredictive analysisSentiment analysisTwitter data

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  • Training and evaluation on a dataset of 5200 English tweets containing product opinions (positive, negative, neutral).
  • Optimization and comparative analysis of the performance of different machine learning algorithms.

    • Main Results:

    • Machine learning effectively extracts and analyzes unstructured social media text for sentiment.
    • The study determined the most effective sentiment classification methods for product discussions.
    • Sentiment analysis of social media data offers significant benefits for businesses.

    Conclusions:

    • Social media sentiment analysis is a viable and useful business strategy.
    • Companies can enhance client approaches and marketing by analyzing consumer attitudes.
    • Understanding customer opinions through social media leads to improved products, services, and customer loyalty.