Sentiment Analysis on Movie Review Data Using Ensemble Vote Classifier Technique

Main Article Content

A. Naresh
P. Venkata Krishna
M Bhavsingh

Abstract

Sentiment analysis is a procedure of investigating sentiments or feelings expressed in the text. It classifies whether the given text is positive or negative or sometimes neutral also, based on the classification level on a given document or sentence. To encourage the customers in better decision making and to find perspectives on others and furthermore helps in choosing the buy with the opinion of different customers. In this article a novel ensemble vote classifier technique with logistic regression, random forest and XGB classifiers is proposed and sentiment analysis on real-time movie review data is analyzed using proposed technique. Experimental results shows improved accuracy with balanced class weight compared to without balanced class weight.

Article Details

How to Cite
[1]
A. Naresh, P. Venkata Krishna, and M Bhavsingh, “Sentiment Analysis on Movie Review Data Using Ensemble Vote Classifier Technique”, Int. J. Comput. Eng. Res. Trends, vol. 8, no. 7, pp. 112–116, Jul. 2021.
Section
Research Articles

References

B. Pang and L. Lee, “Opinion mining and sentiment analysis”, Foundation and Trends in Information Retrieval, vol. 2, 2008, pp. 1–135.

B. Liu , “Sentiment analysis and opinion mining”, Morgan and Claypool Publishers, Synthesis Lectures on Human Language Technologies, vol. 5 2012, pp. 1–167.

Y. Mejova, “Sentiment Analysis: An Overview”, 2009, retrieved from https://www.researchgate.net/publication/264840229_Sentiment_Analysis_An_Overview

Y. Zhang, and B. Wallace, “A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification”, IJCNLP, 2017.

Nimirthi, Pradeepthi, P. Venkata Krishna, Mohammad S. Obaidat, and V. Saritha. "A framework for sentiment analysis based recommender system for agriculture using deep learning approach." In Social Network Forensics, Cyber Security, and Machine Learning, pp. 59-66. Springer, Singapore, 2019.

G. Preethi, P.V. Krishna, S. Obaidat Mohammad, V. Saritha and Y. Sumanth, “ Application of Deep Learning to Sentiment Analysis for recommender system on cloud”, International Conference on Computer, Information and Telecommunication Systems (CITS) , 2017, pp. 93-97.

Kavitha, Modepalli, and P. Venkata Krishna. "IoT-Cloud-Based Health Care System Framework to Detect Breast Abnormality." Emerging Research in Data Engineering Systems and Computer Communications. Springer, Singapore, 2020. 615-625.

Y. He and D. Zhou, “Self-training from labeled features for sentiment analysis”, Information Processing and Management, vol. 47, 2011, pp. 606–616.

F. Xianghua, L. Guo, G. Yanyan and W. Zhiqiang, “Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon”, Knowledge Based Systems, vol.37, 2013, pp. 186–195.

Huang, C. F., Hsieh, T. N., Chang, B. R., & Chang, C. H. (2012, September). A comparative study of regression and evolution-based stock selection models for investor sentiment. In 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications (pp. 73-78). IEEE.

Hoque, M. T., Islam, A., Ahmed, E., Mamun, K. A., & Huda, M. N. (2019, February). Analyzing Performance of Different Machine Learning Approaches With Doc2vec for Classifying Sentiment of Bengali Natural Language. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-5). IEEE.

Khan, S., Chopra, K., & Sharma, P. (2020, February). Brand Review Prediction using User Sentiments: Machine Learning Algorithm. In 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-8). IEEE.

Oyebode, Oladapo, FelwahAlqahtani, and Rita Orji. "Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews." IEEE Access 8 (2020): 111141-111158.

Aziz, A. A., Starkey, A., & Bannerman, M. C. (2017, September). Evaluating cross domain sentiment analysis using supervised machine learning techniques. In 2017 Intelligent Systems Conference (IntelliSys) (pp. 689-696). IEEE.

R. B. Shamantha, S. M. Shetty and P. Rai, "Sentiment Analysis Using Machine Learning Classifiers: Evaluation of Performance," 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore, 2019, pp. 21-25, doi: 10.1109/CCOMS.2019.8821650.

Karthika, P., R. Murugeswari, and R. Manoranjithem. "Sentiment Analysis of Social Media Network Using Random Forest Algorithm." 2019 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2019.

Miro?czuk, Marcin &Protasiewicz, Jaroslaw. (2018). A Recent Overview of the State-of-the-Art Elements of Text Classification. Expert Systems with Applications. 106. 10.1016/j.eswa.2018.03.058.

Jonnavithula, S. K., Jha, A. K., Kavitha, M., & Srinivasulu, S. (2020, October). Role of machine learning algorithms over heart diseases prediction. In AIP Conference Proceedings (Vol. 2292, No. 1, p. 040013). AIP Publishing LLC.

Matthew Chang, Chung Keung Poon,Using phrases as features in email classification,Journal of Systems andSoftware,Volume 82, Issue 6,2009,Pages 1036-1045,ISSN 0164-1212,https://doi.org/10.1016/j.jss.2009.01.013.