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Sentiment Analysis

Gaging Opinions of Large Groups

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Applied Data Science in Tourism

Part of the book series: Tourism on the Verge ((TV))

Abstract

Sentiment analysis attempts to measure peoples’ feelings such as joy, sadness, anger, and similar, usually expressed in a written text. Since the decision-making process is largely based on knowledge about opinions of others, the sentiment analysis has become central in all areas of business dealing with customers and in many social sciences. While traditionally sentiment analysis was conducted manually by trained raters, the growth of the Big Data, and especially the product reviews in the social media prompted development of the computer sentiment analysis algorithms. Two main approaches to sentiment analysis are lexicon based and machine learning. The lexicon-based approach is based on a pre-existing dictionary of words together with their sentiment orientation and strength; it is fast in implementation yet returns modest performance. In a machine learning approach, a sample of documents from the same domain is manually classified according to the sentiment expressed and then used to train and validate a machine learning algorithm. While this approach requires significant initial expenses in terms of human time and computer resources, it also typically results in a better-performing algorithm. Whichever approach is used, an accurate validation of model performance using appropriate indicators is a must.

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Author information

Authors and Affiliations

  1. Department of Tourism, Hospitality and Event Management, University of Florida, Gainesville, United States

    Andrei P. Kirilenko, Luyu Wang & Svetlana O. Stepchenkova

Authors
  1. Andrei P. Kirilenko
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  2. Luyu Wang
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  3. Svetlana O. Stepchenkova
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Corresponding author

Correspondence to Andrei P. Kirilenko .

Editor information

Editors and Affiliations

  1. Innovation & Management in Tourism, Salzburg University of Applied Sciences, Urstein (Puch), Salzburg, Austria

    Roman Egger

Further Readings and Other Sources

Further Readings and Other Sources

  • Books: “Sentiment Analysis: Mining Opinions, Sentiments, and Emotions” by Bing Liu (2020) is a good introductory text covering all important aspects of computational analysis of sentiment and emotions as well as the most popular algorithmic approaches and major developments in the field.

  • Videos: “Sentiment Analysis: extracting emotion through machine learning” by Andy Kim. A 10-minutes TED talk introducing sentiment analysis. https://www.youtube.com/watch?v=n4L5hHFcGVk

  • Web sites: Medium.com, towardsdatascience.com, and KDnuggets.com sites have an excellent set of AI articles including those covering sentiment analysis.

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Kirilenko, A.P., Wang, L., Stepchenkova, S.O. (2022). Sentiment Analysis. In: Egger, R. (eds) Applied Data Science in Tourism. Tourism on the Verge. Springer, Cham. https://doi.org/10.1007/978-3-030-88389-8_17

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