Sentiment Analysis Based on a Deep Stochastic Network and Active Learning
|Tulsi Jain1 , Kushagra Agarwal2 , Ronil Pancholia3|
1 Dept. of CSE, Indian Institute of Technology (IIT), Delhi, India.
2 Dept. of CSE, Indian Institute of Technology (IIT), Delhi, India.
3 Dept. of CSE, Birla Institute of Technology and Science, Pilani, India.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-9 , Page no. 1-6, Sep-2017
Online published on Sep 30, 2017
Copyright © Tulsi Jain, Kushagra Agarwal, Ronil Pancholia . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Tulsi Jain, Kushagra Agarwal, Ronil Pancholia, “Sentiment Analysis Based on a Deep Stochastic Network and Active Learning”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.1-6, 2017.
MLA Style Citation: Tulsi Jain, Kushagra Agarwal, Ronil Pancholia "Sentiment Analysis Based on a Deep Stochastic Network and Active Learning." International Journal of Computer Sciences and Engineering 5.9 (2017): 1-6.
APA Style Citation: Tulsi Jain, Kushagra Agarwal, Ronil Pancholia, (2017). Sentiment Analysis Based on a Deep Stochastic Network and Active Learning. International Journal of Computer Sciences and Engineering, 5(9), 1-6.
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|This paper proposes a novel approach for sentiment analysis. The growing importance of sentiment analysis commensurate with the use of social media such as reviews, forum discussions, blogs, microblogs like Twitter, and other social networks. We require efficient and higher accuracy algorithms in sentiment polarity classification as well as sentiment strength detection. In comparison to pure vocabulary based system, deep learning algorithms show significantly higher performance. The goal of this research is to modify a Recurrent Neural Network (RNN) with Gated Recurrent Unit (GRU) by introducing stochastic depth in a hidden layer and comparing it with baseline Naïve Bayes, vanilla RNN and GRU-RNN models. To improve our results, we also incorporated Active Learning with Uncertainty Sampling approach. Movie review dataset from Rotten Tomatoes was used, the dataset includes 215,154 fine grained labelled phrases in addition to 11,855 full sentences. We performed pre-processing on the data and used an embedding matrix with pre-trained word vectors as features for training our model. These word vectors were generated using character level n-grams with fasttext on Wikipedia data.|
|Key-Words / Index Term :|
|Fasttext, Recurrent Neural Network, Gated Recurrent Unit, Active Learning|
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