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Statistical Natural Language Processing for Sentiment Analysis

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Introduction to Data Science

Part of the book series: Undergraduate Topics in Computer Science ((UTICS))

Abstract

In this chapter, we will perform sentiment analysis from text data. Generally, sentiment analysis is performed based on the processing of natural language, the analysis of text and computational linguistics. Although data can come from different data sources, in this chapter we will analyze sentiment in text data, using two particular text data examples: one from film critics, where the text is highly structured and maintains text semantics; and another example coming from social networks, where the text can show a lack of structure and users may use text abbreviations. We will review basic mechanisms required to perform sentiment analysis, including data cleaning, producing a general representation of the text, and performing some statistical inference on the text represented to determine positive and negative sentiments.

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Notes

  1. 1.

    https://docs.python.org/2/library/re.html.

  2. 2.

    https://textblob.readthedocs.io/en/dev/.

  3. 3.

    Such as those provided in http://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html.

  4. 4.

    http://www.sananalytics.com/lab/twitter-sentiment/.

References

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Acknowledgements

This chapter was co-written by Sergio Escalera and Santi Seguí.

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Correspondence to Laura Igual .

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Igual, L., Seguí, S. (2017). Statistical Natural Language Processing for Sentiment Analysis. In: Introduction to Data Science. Undergraduate Topics in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-50017-1_10

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  • DOI: https://doi.org/10.1007/978-3-319-50017-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50016-4

  • Online ISBN: 978-3-319-50017-1

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