Abstract
Word of mouth has a great impact on commercial planning and decision-making. Social media is considered as one of the greatest media to spread customer’s opinion about product. Twitter in particular serves as a platform to share people opinion with the words. Decision makers nowadays are seeking analysis approaches on customer tweets to classify whether a customer is satisfied or unhappy. But the enormous number of tweets per seconds and the live streaming of twitter require big data processors in order to support decision-making. In this paper, we propose a recommender system that helps decision makers to fetch customer streaming tweets and classifies their opinion within seconds. We aim to achieve that by applying Naïve Bayes algorithm using big data machine learning approach, Apache Hadoop and Mahout tools are used. The result of our finding is a recommender system that can be used to classify any new customer tweets. The accuracy of the model is 99.39% which promises accurate results in identifying negative or positive customer opinion about a product in a tweet.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Data Analytics and Sentiment Analysis (2016). http://conferences.unicom.co.uk/data-and-sentiment-analysis/
Das, T.K., Acharjya, D.P., Patra, M.R.: Opinion mining about a product by analyzing public tweets in Twitter, pp. 3–6 (2014)
Vishal, A., Sonawane, P.: Sentiment analysis of twitter data: a survey of techniques. Int. J. Comput. Appl. (0975–8887) 139(11), 5–15 (2016)
Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation, pp. 1320–1326 (2010)
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the ACL 2011 Workshop on Languages in Social Media, pp. 30–38 (2011)
Neethu, M.S., Rajashree, R.: Sentiment analysis in twitter using machine learning techniques. In: 4th ICCCNT, Tiruchengode, India. IEEE (2013)
Gautam, G., Yadav, D.: Sentiment analysis of twitter data using machine learning approaches and semantic analysis. In: Seventh International Conference on Contemporary Computing (IC3), India. IEEE (2014)
Amolik, A., Jivane, N., Bhandari, M., Venkatesan, M.: Twitter sentiment analysis of movie reviews using machine learning techniques. Int. J. Eng. Technol. (IJET) 7, 1–7 (2016)
Go, A., Bhayani, R., Huang, L.: Twitter sentiment classification using distant supervision processing, pp. 1–6 (2009)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (1999)
Geser, H.: Tweeted thoughts and twittered relationships. In: Sociology in Switzerland: Toward Cybersociety and Vireal Social Relations, Zuerich, February 2009
https://developer.twitter.com/en/docs/basics/things-every-developer-should-know
Blasch, E., et al.: Scalable sentiment classification for Big data analysis using Naïve Bayes classifier (2013)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Operating Systems Design & Implementation, OSDI 2004, Berkeley, CA, USA, vol. 6. USENIX Association (2004)
Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)
Apache Mahout: scalable machine learning and data mining. http://mahout.apache.org/
The R Project for Statistical Computing. https://www.rstudio.com/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Alharbi, A.N., Alnnamlah, H., Liyakathunisa (2018). Classification of Customer Tweets Using Big Data Analytics. In: Alenezi, M., Qureshi, B. (eds) 5th International Symposium on Data Mining Applications. Advances in Intelligent Systems and Computing, vol 753. Springer, Cham. https://doi.org/10.1007/978-3-319-78753-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-78753-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-78752-7
Online ISBN: 978-3-319-78753-4
eBook Packages: EngineeringEngineering (R0)