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A Real-Time Aspect-Based Sentiment Analysis System of YouTube Cooking Recipes

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Machine Learning Paradigms: Theory and Application

Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

Abstract

Nowadays, uploading, searching and downloading cooking recipes, as well as their rating and reviewing have become a daily habit. Millions of reviews seek for exchanging recipes over YouTube. A user spends a lot of time searching for the best cooking recipe through users’ comments. Opinion Mining and Sentiment Analysis are critical tools for information-gathering to find out what people are thinking. In this chapter, we introduce a sentient based real-time system which mines YouTube meta-data (Likes, Dislikes, views, and comments) in order to extract important cooking recipes features and identify opinions polarity according to these extracted features. To improve the performance of our system, we construct a cooking recipe lexicon and propose some algorithms that constructed on sentiment bags, based on particular words related to food emoticons and injections.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technologies, Université Kasdi Merbah Ouargla, BP 511 Route de Ghardaia, 30 000, Ouargla, Algeria

    Randa Benkhelifa, Nasria Bouhyaoui & Fatima Zohra Laallam

Authors
  1. Randa Benkhelifa
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  2. Nasria Bouhyaoui
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  3. Fatima Zohra Laallam
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Corresponding author

Correspondence to Randa Benkhelifa .

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Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Benkhelifa, R., Bouhyaoui, N., Laallam, F.Z. (2019). A Real-Time Aspect-Based Sentiment Analysis System of YouTube Cooking Recipes. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_11

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