HYBRID ARCHITECTURE FOR SENTIMENT ANALYSIS USING DEEP LEARNING

Article Sidebar

PDF
Published: Feb 23, 2018
DOI: https://doi.org/10.26483/ijarcs.v9i1.5388
Keywords:
Sentiment Analysis, Deep Learning, CNN, RNN, Word Embeddin, Word2Vec

Main Article Content

Prssanna Digant Desai
Mukesh Patel School of Technology Management and Engineering
Simran Deshmukh
Mukesh Patel School of Technology Management and Engineering
Surbhi Gawande
Mukesh Patel School of Technology Management and Engineering
Arnav Chakravarthy
Mukesh Patel School of Technology Management and Engineering
Ishani Saha
Mukesh Patel School of Technology Management and Engineering

Abstract

Sentiment analysis involves classifying text into positive, negative and neutral classes according to the emotions expressed in the text. Extensive study has been carried out in performing sentiment analysis using the traditional ‘bag of words’ approach which involves feature selection, where the input is given to classifiers such as Naive Bayes and SVMs. A relatively new approach to sentiment analysis involves using a deep learning model. In this approach, a recently discovered technique called word embedding is used, following which the input is fed into a deep neural network architecture. As sentiment analysis using deep learning is a relatively unexplored domain, we plan to perform in-depth analysis into this field and implement a state of the art model which will achieve optimal accuracy. The proposed methodology will use a hybrid architecture, which consists of CNNs (Convolutional Neural Networks) and RNNs (Recurrent Neural Networks), to implement the deep learning model on the SAR14 and Stanford Sentiment Treebank data sets.

Downloads

Download data is not yet available.

Article Details

Issue
Vol. 9 No. 1 (2018): January-February 2018
Section
Articles

COPYRIGHT

Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
  • The journal allows the author(s) to retain publishing rights without restrictions.
  • The journal allows the author(s) to hold the copyright without restrictions.

References

Arman S. Zharmagambetov, Alexandr A. Pak, “Sentiment analysis of a document using deep learning approach and decision treesâ€, Electronics Computer and Computation (ICECCO), 2015 Twelve International Conference, 27-30 Sept. 2015

Cameron Godbout, “Recurrent Neural Networks for Beginnersâ€

Adit Deshpande, “Perform sentiment analysis with LSTMs, using TensorFlowâ€, July 13, 2017

Abdalraouf Hassan, Ausif Mahmood, “Deep learning for sentence classificationâ€, Systems, Applications and Technology Conference (LISAT), 2017 IEEE Long Island

Christopher Olah, “Understanding LSTM Networksâ€, August 27, 2015

Abdalraouf Hassan, Ausif Mahmood “Efficient Deep Learning Model for Text Classification Based on

Recurrent and Convolutional Layersâ€, 2017 16th IEEE International Conference on Machine Learning and Applications

Xi Ouyang, Pan Zhou, Cheng Hua Li, Lijun Liu, “Sentiment Analysis Using Convolutional Neural Networkâ€, 2015 IEEE International Conference on Computer and Information

G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,†Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955.