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

  • Randa BenkhelifaEmail author
  • Nasria Bouhyaoui
  • Fatima Zohra Laallam
Part of the Studies in Computational Intelligence book series (SCI, volume 801)


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.


YouTube comments Opinion mining Feature extraction Opinion extraction Subjectivity Emoticon Injection 


  1. 1.
    Al-Smadi, M., Talafha, B., Al-Ayyoub, M., Jararweh, Y.: Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews. Int. J. Mach. Learn. Cybern. 1–13 (2018)Google Scholar
  2. 2.
    Al-Smadi, M., Al-Ayyoub, M., Jararweh, Y., Qawasmeh, O.: Enhancing aspect-based sentiment analysis of Arabic hotels’ reviews using morphological, syntactic and semantic features. Inf. Process. Manage. (2018)Google Scholar
  3. 3.
    Benkhelifa, R., Laallam, F.Z.: Facebook posts text classification to improve information filtering. In: Proceedings of the 12th International Conference on Web Information Systems and Technologies, pp. 202–207. Rome, Italy (2016)Google Scholar
  4. 4.
    Benkhelifa, R., Laallam, F.Z.: Opinion extraction and classification of real-time youtube cooking recipes comments. In: International Conference on Advanced Machine Learning Technologies and Applications, pp. 395–404. Springer, Cham (2018)Google Scholar
  5. 5.
    Bianchini, D., De Antonellis, V., De Franceschi, N., Melchiori, M.: PREFer: a prescription-based food recommender system. Comput. Standards Interf. 54, 64–75 (2017)CrossRefGoogle Scholar
  6. 6.
    Chaturvedi, I., Ragusa, E., Gastaldo, P., Zunino, R., Cambria, E.: Bayesian network based extreme learning machine for subjectivity detection. J. Franklin Inst. (2017)Google Scholar
  7. 7.
    Choi, Y., Cardie, C.: Hierarchical sequential learning for extracting opinions and their attributes. In: Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-2010), pp. 268–274 (2010)Google Scholar
  8. 8.
    Dave, K., Lawrence, S., Pennock, D.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of the 12th International Conference on World Wide Web, ACM, New York, NY, USA, WWW ’03Google Scholar
  9. 9.
    Dey, K., Shrivastava, R., Kaushik, S.: Twitter stance detection-a subjectivity and sentiment polarity inspired two-phase approach. In: SENTIRE Workshop, ICDM, Nov 2017Google Scholar
  10. 10.
    Durant, K.T., Smith, M.D.: Mining sentiment classification from political web logs, WEBKDD ’06. Philadelphia, Pennysylvania, USA, ACM 1-59593-4448 (2006)Google Scholar
  11. 11.
    García-Pablos, A., Cuadros, M., Rigau, G.: W2VLDA: almost unsupervised system for aspect based sentiment analysis. Expert Syst. Appl. 91, 127–137 (2018)CrossRefGoogle Scholar
  12. 12.
    Hamouda, S.B., Akaichi. J.: Social networks’ text mining for sentiment classification: the case of facebook’ statuses updates in the “Arabic Spring” Era”. Int. J. Appl. Innov. Eng. Manage. (2013)Google Scholar
  13. 13.
    Höpken, W., Fuchs, M., Menner, T., Lexhagen, M.: Sensing the online social sphere using a sentiment analytical approach. In: Analytics in Smart Tourism Design, pp. 129–146. Springer International Publishing (2017)Google Scholar
  14. 14.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, New York, NY, USA, KDD (2004)Google Scholar
  15. 15.
    Kamal, A.: Subjectivity classification using machine learning techniques for mining feature-opinion pairs from web opinion sources, New Delhi—110025, IndiaGoogle Scholar
  16. 16.
    Kang, Y., Zhou, L.: RubE: rule-based methods for extracting product features from online consumer reviews. Inf. Manag. 54(2), 166–176 (2017)CrossRefGoogle Scholar
  17. 17.
    Kübler, S., Liu, C., Sayyed, Z.A.: To use or not to use: feature selection for sentiment analysis of highly imbalanced data. Nat. Lang. Eng. 24(1), 3–37 (2018)CrossRefGoogle Scholar
  18. 18.
    Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Human Lang. Technol. 5(1), 1–167 (2012)CrossRefGoogle Scholar
  19. 19.
    Liu, C., Guo, C., Dakota, D., Rajagopalan, S., Li, W., K¨ubler, S.: My curiosity was satisfied, but not in a goodway: predicting user ratings for online recipes. In: Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 12–21. Dublin, Ireland, 24 Aug 2014Google Scholar
  20. 20.
    Liu, Q., Gao, Z., Liu, B., Zhang, Y.: Automated rule selection for aspect extraction in opinion mining. In: Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI ’15, pp. 1291–1297. AAAI Press (2015)Google Scholar
  21. 21.
    Lisa Hankin, L.: The effects of user reviews on online purchasing behavior across multiple product categories. Master’s Final Project Report, UC Berkeley School of Information (2007)Google Scholar
  22. 22.
    Lovins, J.B.: Development of a stemming algorithm. Mech. Trans. Comput. Linguist. (1968)Google Scholar
  23. 23.
    Manek, A.S., Shenoy, P.D., Mohan, M.C., Venugopal, K.R.: Aspect term extraction for sentiment analysis in large movie reviews using Gini index feature selection method and SVM classifier. World Wide Web 20(2), 135–154 (2017)CrossRefGoogle Scholar
  24. 24.
    Ozaki, T., Gao, X., Mizutani, M.: Extraction of characteristic sets of ingredients and cooking actions on cuisine type. In: 2017 31st International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 509–513. IEEE (2017)Google Scholar
  25. 25.
    Pang, A., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts In: Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL ’04), pp. 271–278. Barcelona, ES (2004)Google Scholar
  26. 26.
    Piryani, R., Madhavi, D., Singh, V.K.: Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Inf. Process. Manage. 53(1), 122–150 (2017)CrossRefGoogle Scholar
  27. 27.
    Poria, S., Cambria, E., Ku, L.W., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP), pp. 28–37. Association for Computational Linguistics and Dublin City University (2014)Google Scholar
  28. 28.
    Pugsee, P., Niyomvanich, M.: Suggestion analysis for food recipe improvement. In: Proceeding of the 2015 International Conference on Advanced Informatics: Concepts, Theory and Application (ICAICTA) (2015)Google Scholar
  29. 29.
    Rana, T.A., Cheah, Y.N.: A two-fold rule-based model for aspect extraction. Expert Syst. Appl. 89, 273–285 (2017)CrossRefGoogle Scholar
  30. 30.
    Rana, T.A., Cheah, Y.N.: Exploiting sequential patterns to detect objective aspects from online reviews. In: 2016 International Conference on Advanced Informatics: Concepts, Theory And Application (ICAICTA), pp. 1–5. IEEE, Aug 2016Google Scholar
  31. 31.
    Rana, T.A., Cheah, Y.N.: Improving aspect extraction using aspect frequency and semantic similarity-based approach for aspect-based sentiment analysis. In: International Conference on Computing and Information Technology, pp. 317–326. Springer, Cham, July 2017Google Scholar
  32. 32.
    Rao, S., Kakkar, M.: A rating approach based on sentiment analysis. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, pp. 557–562. IEEE, Jan 2017Google Scholar
  33. 33.
    Rokicki, M., Herder, E., Trattner, C.: How editorial, temporal and social biases affect online food popularity and appreciation. In: ICWSM, pp. 192–200 (2017)Google Scholar
  34. 34.
    Sebastiani, A.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Singh, A., Shukla, N., Mishra, N.: Social media data analytics to improve supply chain management in food industries. Transp. Res. Part E Logist. Transp. Rev. (2017)Google Scholar
  36. 36.
    Tan, S.S., Na, J.C.: Mining semantic patterns for sentiment analysis of product reviews. In: International Conference on Theory and Practice of Digital Libraries, pp. 382–393. Springer, Cham, Sept 2017Google Scholar
  37. 37.
    Verma, S., Bhattacharyya, P.: Incorporating semantic knowledge for sentiment analysis. In: Proceedings of International Conference on Natural Language Processing (2009)Google Scholar
  38. 38.
    Raut, V.B., et al.: Survey on opinion mining and summarization of user reviews on web. (IJCSIT) Int. J. Comput. Sci. Inf. Technol. 5(2), 1026–1030 (2014)Google Scholar
  39. 39.
    Wilson, T.: Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states, University of Pittsburgh (2008)Google Scholar
  40. 40.
    Witten, H.A., Frank, E.: Data mining: practical machine learning tools and techniques with java implementations, Morgan Kaufmann (2000)Google Scholar
  41. 41.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  42. 42.
    Zhang, X., Zhu, F.: The influence of online consumer reviews on the demand for experience goods: the case of video games. In: 27th International Conference on Information Systems (ICIS). Milwaukee, AISPress (2006)Google Scholar
  43. 43.
    Zeng, L., Li, F.: A classification-based approach for implicit feature identification. In: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. 12th China National Conference, CCL 2013 and First International Symposium, NLP-NABD 2013, Suzhou, China, 10–12 Oct 2013, Proceedings, volume 8202 of Lecture Notes in Computer Science, pp. 190–202 (2013)Google Scholar
  44. 44.
    Zhen, H., Chang, K., Kim, J.: Implicit feature identification via co-occurrence association rule mining. In: Computational Linguistics and Intelligent Text Processing. 12th International Conference, CICLing 2011, Tokyo, Japan, 20–26 Feb 2011. Proceedings, Part I, volume 6608 of Lecture Notes in Computer Science, pp. 393–404 (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Randa Benkhelifa
    • 1
    Email author
  • Nasria Bouhyaoui
    • 1
  • Fatima Zohra Laallam
    • 1
  1. 1.Department of Computer Science and Information TechnologiesUniversité Kasdi Merbah OuarglaOuarglaAlgeria

Personalised recommendations