Abstract
This paper provides an insight to one of the recent additions in the turf of Machine Learning culture - the process of learning representation or features, known as Deep Learning. It is highly anticipated that Deep Learning will fare much better than the traditional machine learning algorithms not only because of scalability but also of its ability to perform automatic feature extraction from raw data. This paper deals with the analyzing of sentiments on a set of movie reviews, which is considered to be the most demanding facet of NLP’s. In this paper, Google’s algorithm Word2Vec has been applied on a large movie review dataset to classify text so that the semantic associations between the terms stay conserved. A comparative study of the performances of some notable clustering algorithms is demonstrated concerning their application involving a variable number of features and classifier types as well as variable number of clusters.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bengioy, Y., Courville, A., Vincenty, P.: Representation Learning: A Review and New Perspectives, 23 April 2014. arXiv:1206.5538v3 [cs.LG]
Bengio, Y.: Learning deep architectures for AI. Found. TrendsR Mach. Learn. 2(1), 1–127 (2009). https://doi.org/10.1561/2200000006
Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5, 1–167 (2012)
Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. Paper Presented at the Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, Philadelphia, PA (2002)
Somasundaran, S., Wiebe, J.: Recognizing stances in ideological on‐line debates. In: Proceedings of the Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text (2010)
Hu, M., Liu, B.: Mining and summarizing customer reviews. Paper Presented at the Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)
Kim, S.‐M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. Paper Presented at the Proceedings of the Workshop on Sentiment and Subjectivity in Text (2006)
Deng, L., Wiebe, J.: Sentiment propagation via implicature constraints. In: Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pp. 377–385. EACL (2014)
Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 815–824. ACM (2011)
Ling, P., Geng, C., Menghou, Z., Chunya, L.: What Do Seller Manipulations of Online Product Reviews Mean to Consumers? (HKIBS Working Paper Series 070-1314) Hong Kong Institute of Business Studies, Lingnan University, Hong Kong (2014)
Rokach, L.: Ensemble methods for classifiers. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 957–980. Springer, US (2005). http://dx.doi.org/10.1007/0-387-25465-X_45
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data. In: Proceedings of the Workshop on Languages in Social Media, pp. 30–38. Association for Computational Linguistics (2011)
Sharma, A., Dey, S.: A comparative study of feature selection and machine learning techniques for sentiment analysis. In: Proceedings of the 2012 ACM Research in Applied Computation Symposium, pp. 1–7. ACM (2012)
Kouloumpis, E., Wilson, T., Moore, J.D.: Twitter sentiment analysis: the good the bad and the omg! In: ICWSM, vol. 11, pp. 538–541 (2011)
Kaggle: Bag of Words Meets Bags of Popcorn. https://www.kaggle.com/c/word2vec-nlp-tutorial/data
Cython C-Extensions for Python. http://cython.org/
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
Chakraborty, K., Bhattacharyya, S., Bag, R., Hassanien, A.E. (2018). Comparative Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Approach. 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_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-74690-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74689-0
Online ISBN: 978-3-319-74690-6
eBook Packages: EngineeringEngineering (R0)