Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

Sentiment analysis
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Contents:

  • ISBN 13: 9781107017894
  • Sentiment analysis uses
  • Contextual understanding and tone
  • Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

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    Sentiment analysis uses

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    Contextual understanding and tone

    Book Review. Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Bing Liu. (University of Illinois at Chicago). Cambridge University Press, Editorial Reviews. Review. 'As a whole, this book serves as a useful introduction to sentiment analysis along with in-depth discussions of linguistic phenomena.

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    Sentiment Analysis and Opinion Mining. Bing Liu. Lifelong Machine Learning. Zhiyuan Chen. Sentiment Analysis in Social Networks. Federico Alberto Pozzi. How to write a great review. The review must be at least 50 characters long. The title should be at least 4 characters long. He has published extensively in top conferences and journals, and his research has been cited on the front page of the New York Times.

    Opinions, Sentiment, and Emotion in Text. Bing Liu. Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society.

    It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. See our Privacy Policy and User Agreement for details. Published on Jan 7, SlideShare Explore Search You.

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    Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

    In this course, you will develop your text mining skills using tidy data principles. Often, sentiment is computed on the document as a whole or some aggregations are done after computing the sentiment for individual sentences. Thanks to the emotional analysis in text we can know the affective value of the words and the emotional shit they contain. In contrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. The task of automatically classifying a text written in a natural language into a positive or negative feeling, opinion or subjectivity Pang and Lee, , is sometimes so complicated that even different human annotators disagree on the classification to be assigned to a given text. Read more

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