Review Length Aware Hybrid Approach to Sentiment Analysis

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Babaljeet Kaur 1,* Naveen Kumari 1

1. Department of Computer Science Engineering, Punjabi University Regional Centre, Mohali, India

* Corresponding author.


Received: 11 Jul. 2016 / Revised: 23 Aug. 2016 / Accepted: 6 Oct. 2016 / Published: 8 Nov. 2016

Index Terms

Sentiment Analysis, SuperFetch Reviews, Support Vector Machine, K-Nearest Neighbor


Sentiment analysis is a popular research problem to find out within the natural language processing that is dealing with identifying the sentiments or mood of people’s towards elements such as product, text, services and the technology. However, there are few researches conducted on the sentiment analysis of technical article review, so to overcome this deficiency conducts the sentiment analysis over the technical article review and classifying the sentence by overall sentiments that is representing the review is positive or negative. The paper presents the combination of SVM and KNN and find out how much given article sound technically good. The proposed technique is compared with other existing techniques and results shows that the proposed technique is better as compared to the other technique.

Cite This Paper

Babaljeet Kaur, Naveen Kumari, "Review Length Aware Hybrid Approach to Sentiment Analysis", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.11, pp.58-64, 2016. DOI:10.5815/ijmecs.2016.11.08


[1]O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A Hybrid Approach to Sentiment Analysis with Benchmarking Results,” In Proceedings of 29th International Conference on Industrial Engineering and Application of Artificial Intelligence and Expert Systems, IEA/AIE, LNAI 9799, 2016, pp. 242-254.
[2]V. Nandi, and S. Agrawal, “Political Sentiment Analysis using Hybrid Approach,” International Research Journal of Engineering and Technology, vol. 3, issue. 5, pp. 1621-1627, May. 2016.
[3]A. Tripathi, A. Agrawal, and S. K. Rath, “ Classification of Sentimental Reviews using Machine learning Techniques,” in Proceedings of 3rd International Conference on Recent Trends in Computing, ICRTC, 2015, pp. 821-829.
[4]Nagamma, et al., “An Improved Sentiment Analysis Of Online Movie Reviews Based On Clustering For Box-Office Prediction,” in Proceedings of the International Conference on Computing, Communication and Automation (ICCCA2015), IEEE, pp. 933-937.
[5]Y. Sharma, V. Mangat, and M. Kaur, “Sentiment Analysis & Opinion Mining,” International Journal of Soft Computing and Artificial Intelligence, vol.3, issue. 1, pp. 59-62, 2015.
[6]C. Li, et al., “Recursive Deep Learning for Sentiment Analysis over Social Data,” in Proceedings of International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), IEEE/WIC/ACM, 2014, pp. 180-185.
[7]N. Zainuddin, and A. Selamat, “Sentiment Analysis using Support Vector Machine,” in Proceedings of IEEE International Conference on Computer, Communication and Control Technology, I4CT, 2014, pp. 333-337.
[8]C. Wang, et al., “ SentiView: Sentiment Analysis and Visualization for Internet Popular Topics,” IEEE Transactions on Human-Machine Systems, vol. 43, no. 6, Nov. 2013, pp. 620-630.
[9]U. Grandi, A. Loreggia, F. Rossi, and V. Saraswat, “A Borda count for collective sentiment analysis,” Annals of Mathematics and Artificial Intelligence, 22 October 2015, DOI: 10.1007/s10472-015-9488-0.
[10]E. Haddi, X. Liu, and Y. Shi, “The Role of text pre-processing in sentiment analysis,” Information Technology and Quantitative Management, pp.26-32, 2013.
[11]S. M. Vohra, and PROF. J. B. Teraiya, “A Comparative Study of Sentiment Analysis Techniques,” Journal of Information, Knowledge and Research in Computer Engineering, vol. 2, issue. 2, pp. 313-317, Nov-Oct. 2013.
[12]S. Modha, G. S. Pandi, and S. J. Modha, “Automatic sentiment analysis for unstructured data,” International Journal of Advanced Research in Computer Science and Software Engineering, vol.3, pp.91-97, December -2013.
[13]A. Shoukry, and A. Rafea, “A Hybrid Approach for Sentiment Classification of Egyptian Dialect Tweets,” in Proceedings of First International Conference on Arabic Computational Linguistics, 2015, pp. 78-85.
[14]B. Pang, et al., “Thumps up? Sentiment Classification using Machine Learning Techniques,” in Proceedings of Conference on Empirical Methods in Natural Language Processing, EMNLP, Philadelphia, July. 2002, pp. 79-86.