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Machine Learning Technique for Target-Based Sentiment Analysis

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Jyoti Srivastava
Neha Katiyar
Published in
Micro-Electronics and Telecommunication Engineering
Publisher
Springer Singapore

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Abstract

The chapter delves into the critical role of machine learning in sentiment analysis, focusing on the era of vast data sharing on social platforms. It highlights the efficacy of deep learning techniques like CNN and LSTM in evaluating sentiments at both sentence and context levels. The study examines the application of word vectors and deep learning models in sentiment analysis, particularly in lesser-studied languages like Tibetan. It also compares the performance of various machine learning and deep learning models, showcasing the potential of these methods in understanding consumer opinions and improving products or services. The chapter concludes by emphasizing the significance of using emojis as sentiment identifiers and the promise of machine learning in sentiment classification.
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previous chapter PHY Layer Design for MIH in GNU Radio
next chapter Performance Improvement in Target Detection Using Various Techniques in Complex Matched Filter in Radar Communication
Title
Machine Learning Technique for Target-Based Sentiment Analysis
Authors
Jyoti Srivastava
Neha Katiyar
Copyright Year
2021
Publisher
Springer Singapore
Book
Micro-Electronics and Telecommunication Engineering

Print ISBN: 978-981-334-686-4

Electronic ISBN: 978-981-334-687-1

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4687-1_16
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