Abstract
Applications based on Opinion Mining and Sentiment Analysis are critical tools for information-gathering to find out what people are thinking. It is one of the most active research areas in Natural Language Processing. In this paper, we develop a real-time system to extract and classify the YouTube cooking recipes reviews automatically. This system is based on Support vector machine approach and deals with the social media text characteristics. The proposed system collects data in real time from YouTube according to a user request. After it filters opinion texts from the other content, then it classifies it in (positive/negative) opinion. To improve the performance of our system we proposed some algorithms that constructed on sentiment bags, based on emoticons and injections.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kamal, A.: Subjectivity classification using machine learning techniques for mining feature opinion pairs from web opinion sources, New Delhi, India (2013)
Sebastiani, A.: Machine learning in automated text categorization. ACM Comput. Surv. 34, 1–47 (2002)
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 2004), Barcelona, ES, pp. 271–278 (2004)
Bianchini, D., De Antonellis, V., De Franceschi, N., Melchiori, M.: PREFer: a prescription-based food recommender system. Comput. Stand. Interfaces 54, 64–75 (2017)
Liu, C., Guo, C., Dakota, D., Rajagopalan, S., Li, W., Kübler, 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), Dublin, Ireland, 24 August 2014, pp. 12–21 (2014)
Chaturvedi et al.: Bayesian network based extreme learning machine for subjectivity detection. J. Frankl. Inst. (2017). https://doi.org/10.1016/j.jfranklin.2017.06.007
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, WWW 2003. ACM, New York (2003)
Witten, H.A., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (2000)
Höpken, W., Fuchs, M., Menner, T., Lexhagen, M.: Sensing the online social sphere using a sentiment analytical approach. In: Xiang, Z., Fesenmaier, D. (eds.) Analytics in Smart Tourism Design, pp. 129–146. Springer International Publishing, Cham (2017)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004. ACM, New York (2004)
Liu, J., Seneff, S., Zue, V.: Harvesting and summarizing user-generated content for advanced speech-based HCI. IEEE J. Sel. Topics Sig. Process. 6(8), 982–992 (2012)
Lovins, J.B.: Development of a stemming algorithm. Mechanical Translation and Computational Linguistics (1968)
Durant, K.T., Smith, M.D.: Mining sentiment classification from political web logs. In: WEBKDD 2006, Philadelphia, Pennysylvania, USA. ACM (2006). 1-59593-4448
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)
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)
Yu, N., Zhekova, D., Liu, C., Kübler, S.: Do good recipes need butter? Predicting user ratings of online recipes. In: Proceedings of the IJCAI Workshop on Cooking with Computers, Beijing, China (2013)
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, January 2017
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. Manag. (IJAIEM), 2(5), 470–478 (2013)
Verma, S., Bhattacharyya, P.: Incorporating semantic knowledge for sentiment analysis. In: Proceedings of International Conference on Natural Language Processing (2009)
Wilson, T.: Fine-grained subjectivity and sentiment analysis: recognizing the intensity, polarity, and attitudes of private states. University of Pittsburgh (2008)
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, September 2017
Raut, V.B., et al.: Survey on opinion mining and summarization of user reviews on web. Int. J. Comput. Sci. Inf. Technol. (IJCSIT) 5(2), 1026–1030 (2014)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Benkhelifa, R., Laallam, F.Z. (2018). Opinion Extraction and Classification of Real-Time YouTube Cooking Recipes Comments. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-319-74690-6_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74689-0
Online ISBN: 978-3-319-74690-6
eBook Packages: EngineeringEngineering (R0)