Description

Book Synopsis


Table of Contents
1. Introduction to Text and Sentiment Data Analysis 2. Natural Language Processing and Sentiment Analysis: Perspectives from Computational Intelligence 3. Applications and Challenges of Sentiment Analysis in Real Life Scenarios 4. Emotions Recognition of Students from Online and Offline Texts 5. Online Social Network Sensing Models 6. Identifying Sentiments of Hate Speech using Deep Learning 7. An Annotation System to Summarize Medical Corpus using Sentiment based Models 8. Deep learning-based Dataset Recommendation System by employing Emotions 9. Hybrid Deep Learning Architecture Performance on Large English Sentiment Text Data: Merits and Challenges 10. Human-centered Sentiment Analysis 11. An Interactive Tutoring System for Older Adults - Learning with New Apps 12. Irony and Sarcasm Detection 13. Concluding Remarks

Computational Intelligence Applications for Text

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    £103.50

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    RRP £115.00 – you save £11.50 (10%)

    Order before 4pm tomorrow for delivery by Tue 9 Jun 2026.

    A Paperback by Dipankar Das, Anup Kumar Kolya, Abhishek Basu

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      View other formats and editions of Computational Intelligence Applications for Text by Dipankar Das

      Publisher: Elsevier Science
      Publication Date: 7/20/2023 12:00:00 AM
      ISBN13: 9780323905350, 978-0323905350
      ISBN10: 0323905358
      Also in:
      Computing Artificial intelligence (AI) Data capture and analysis

      Description

      Book Synopsis


      Table of Contents
      1. Introduction to Text and Sentiment Data Analysis 2. Natural Language Processing and Sentiment Analysis: Perspectives from Computational Intelligence 3. Applications and Challenges of Sentiment Analysis in Real Life Scenarios 4. Emotions Recognition of Students from Online and Offline Texts 5. Online Social Network Sensing Models 6. Identifying Sentiments of Hate Speech using Deep Learning 7. An Annotation System to Summarize Medical Corpus using Sentiment based Models 8. Deep learning-based Dataset Recommendation System by employing Emotions 9. Hybrid Deep Learning Architecture Performance on Large English Sentiment Text Data: Merits and Challenges 10. Human-centered Sentiment Analysis 11. An Interactive Tutoring System for Older Adults - Learning with New Apps 12. Irony and Sarcasm Detection 13. Concluding Remarks

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