Abstract
Sentiment analysis is a series of methods, techniques, and tools about detecting and extracting subjective information, such as opinion and attitudes, from language. The goal of our project was to classify movies’ reviews, by analyzing the polarity (positive or negative) of each paragraph in a review (Cui et al., Neurocomputing 187:126–132, 2016). We experimented with various RNN models on the Nervana Neon deep learning framework, an open-source framework developed by Nervana Systems, in order to improve accuracy in training and validation data. We experimented with network architecture, hyper parameters (batch size, number of epochs, learning rate, batch normalization, depth, vocabulary size) in order to find out which model works best for sentiment classification. This paper presents our findings and conclusions.
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References
Kowalska, K., Cai, D., Wade, S.: Sentiment analysis of polish texts. Int. J. Comput. Commun. Eng. 1(1), 39–42 (2012)
Chowdhuri, I.K., Latif, S., Hossain, M.S.: Sentiment intensity analysis of informal texts. Int. J. Comput. Appl. 147(10), 24–31 (2016)
Cui, Z., Shi, X., Chen, Y.: Sentiment analysis via integrating distributed representations of variable-length word sequence. Neurocomputing. 187, 126–132 (2016)
Neon: Retrieved Oct. 28, 2018 [Online]. http://neon.nervanasys.com/docs/latest/ (n.d.)
Kabir, I., Latif, S., Saddam, M.: Sentiment intensity analysis of informal texts. Int. J. Comput. Appl. 147(10), 24–31 (2016)
Grefenstette, G.: Explorations in Automatic Thesaurus Discovery. The Springer International Series in Engineering and Computer Science. Springer, New York (2012)
Socher, R., Lin, C.C.-Y., Ng, A.Y., Manning, C.D.: Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th International Conference on Machine Learning
Maas, A.: Large movie review dataset. Retrieved May 18, 2017, from http://ai.stanford.edu/~amaas/data/sentiment (n.d.)
Harer, S., Kadam, S.: Sentiment classification and feature based summarization of movie reviews in mobile environment. Int. J. Comput. Appl. 100(1), 30–35 (2014)
Neon IMDB sentiment classification implementation. Retrieved Oct. 26, 2018, from https://gist.github.com/nervanazoo/976ec931bb4549131ae0
https://hunseblog.wordpress.com/2014/09/15/installing-numpy-and-openblas/
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Bhullar, P.K., Vielma, C., Bein, D., Popa, V. (2019). MeasureOP: Sentiment Analysis of Movies Text Data. In: Latifi, S. (eds) 16th International Conference on Information Technology-New Generations (ITNG 2019). Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-030-14070-0_78
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DOI: https://doi.org/10.1007/978-3-030-14070-0_78
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