Improving Sentiment Analysis using Hybrid Deep Learning Model

Author(s): Avinash Chandra Pandey*, Dharmveer Singh Rajpoot

Journal Name: Recent Advances in Computer Science and Communications
(Formerly: Recent Patents on Computer Science)

Volume 13 , Issue 4 , 2020

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Graphical Abstract:


Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods.

Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost.

Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced.

Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated.

Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Keywords: Sentiment analysis, deep learning, natural language processing, machine learning, concolution neural network, hyper, learning, sentiment lexicons.

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Article Details

Year: 2020
Published on: 19 October, 2020
Page: [627 - 640]
Pages: 14
DOI: 10.2174/2213275912666190328200012
Price: $25

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