book

Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

by D. Jude Hemanth

January 2024

Beginner

254 pages

9h 7m

English

Morgan Kaufmann

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Contents

Abstract1.1 Introduction1.2 Related background1.3 Performance metrics1.4 Tools for sentiment analysis1.5 Trends of sentiment analysis1.6 Challenges1.7 Conclusion1.8 Future directionReferences
Abstract2.1 Introduction2.2 Steps in sentiment analysis2.3 Applications of sentiment analysis2.4 Types of sentiment analysis2.5 Aspect-based sentiment analysis2.6 Performance metrics2.7 Datasets2.8 Future research challenges2.9 ConclusionReferences
Abstract3.1 Introduction3.2 Literature survey3.3 Observations drawn from the literature survey3.4 Open issues and challenges in dimensional sentiment analysis3.5 Future directions3.6 ConclusionReferences
Abstract4.1 Introduction4.2 Human biology of time4.3 Evidence of timelines in the brain: time in linguistic reasoning4.4 Some clues and tests4.5 Conclusions and future workReferences

Abstract5.1 Introduction5.2 Bag of words5.3 Term frequency–inverse document frequency5.4 Glove—word embedding5.5 Long short-term memory5.6 LIME—local interpretable model–agnostic explanations5.7 CodeReferences
Abstract6.1 Introduction6.2 Related works6.3 Proposed model6.4 Results and discussion6.5 ConclusionReferences
Abstract7.1 Introduction7.2 Available tools and resources7.3 Methods and materials7.4 Outlook7.5 ConclusionReferences
Abstract8.1 Introduction8.2 Literature Survey8.3 Proposed System8.4 Computational Experiments and Result Analysis8.5 ConclusionReferences
Abstract9.1 Introduction9.2 Related work9.3 Proposed methodology9.4 Experimental results and discussion9.5 ConclusionReferences
Abstract10.1 Introduction10.2 Datasets10.3 Overall process of sarcasm detection10.4 Sarcasm detection and classification10.5 Sarcasm detection: python code implementation10.6 Evaluation10.7 Results and discussion10.8 ConclusionReferencesFurther reading
Abstract11.1 Introduction11.2 Related work11.3 Dataset description11.4 Methodology11.5 Results11.6 ConclusionReferences
Abstract12.1 Introduction12.2 Literature review12.3 Methodology12.4 Sentiment analysis on twitter data12.5 Forecasting on financial stock data12.6 Implementation of GARCH models12.7 Stimulating stock prices12.8 ConclusionReferences
Abstract13.1 Introduction13.2 Literature survey13.3 Methodology13.4 Experiments and results13.5 ConclusionReferences

Content preview from Computational Intelligence Methods for Sentiment Analysis in Natural Language Processing Applications

Index

Note: Page numbers followed by “f” and “t” refer to figures and tables, respectively.

A

Abstract information, 70
Abstraction, 60–61
Abusive comment detection, in Tamil, 207–226, 209–211
- confusion matrix, implemented models
  - Tamil dataset, 219, 219f
  - Tamil–English dataset, 219, 219f
- dataset, 208, 209t
  - approaches implemented on, 208–209, 210f
  - Tamil dataset, comment category distribution, 215t
  - Tamil–English dataset, comment category distribution, 215t
- Dravidian languages, sentiment analysis for, 212
- hate speech and offensive content identification, 212, 213t
- hate speech, popular categories of, 207, 208f
- methodology, 216–218, 216f
  - Bi-LSTM, 217–218, 218f
  - LSTM, 217, 218f
  - SimpleRNN, 216–217, 217f
- social media comments, homophobia and transphobia detection in, ...

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Publisher Resources

ISBN: 9780443220104