Twitter Sentiment Analysis using XGBoost and Logistic Regression: A Hybrid Approach

Authors

  • Joshi AM Department of CSE, SGBAU, Amaravati, India
  • Prabhune S Department of CSE, SGBAU, Amaravati, India

DOI:

https://doi.org/10.26438/ijcse/v7i8.356360

Keywords:

XGBoost, Logistic Regression, Hybrid Model, Sentiment Analysis, Opinion Mining

Abstract

World Wide Web is the largest source of information and huge information is available on the net. It is the growing tendency in users to express their opinions or thoughts using public opinion sites. Analysing all these opinions manually becomes challenging task so if we can develop the automated system to analyse what people want to say about any product, political party or any other thing it would be of great help. In this work we are trying to make readers life easier by providing the polarity of the reviews from user in automated way with better accuracy. The hybrid model is built using XGBoost and Logistic Regression classifiers and the performance of the hybrid model is compared to both the static models. As per expectation the hybrid model is performing better.

References

[1] “Sentiment Analysis and Opinion Mining”, Bing Liu., Morgan & Claypool Publishers, May 2012

[2] “A Novel, Gradient Boosting Framework for Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A Case Study for Modern Greek”, Vasileios Athanasiou and Manolis Maragoudakis , Artificial Intelligence Laboratory, University of the Aegean, 2017

[3] “Speech and Language Processing.”, Daniel Jurafsky & James H. Martin. Draft of August 24, 2015

[4] “Sentiment Analysis using Logistic Regression and Effective Word Score Heuristic”, Abhilasha Tyagi, Naresh Sharma, International Journal of Engineering and Technology, 2018

[5] Tiangi Chan, Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System”, ACM digital Library, 2016.

[6] Nikolaos Malandrakis, Abe Kazemzadeh, Alexandros Potamianos, Shrikanth Narayanan, “SAIL: A hybrid approach to sentiment analysis”, Second Joint Conference on Lexical and Computational Semantics, Volume 2, 2013.

[7] Ruchika Aggarwal, Latika Gupta, “A Hybrid Approach for Sentiment Analysis using Classification Algorithm”, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.6, June-2017

[8] Oscar Romero, Lombart, “Using Machine Learning Techniques for Sentiment Analysis”, Final project on computer engineering, school of engineering, Universitat Autonoma de Barcelona 2017.

[9] Smitali Desai, Mayuri A. Mehta, “A Hybrid Classification Algorithm to classify Engineering Students’ Problems and Perk”, International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.6, No.2, March 2016.

[10] “Machine Learning of Hybrid Classification Models for Decision Support”, SINTEZA, the use of the internet and development perspectives, 2014.

[11] Dharmendra Sharma1, Suresh Jain, “Evaluation of Stemming and Stop Word Techniques on Text Classification Problem”, International Journal of Scientific Research in Computer Science and Engineering, Volume-3, Issue-2 ISSN: 2320-7639, 2015.

[12] Okechukwu Cornelius, Aru Okereke Eze, “Development of an Optimized Intelligent Machine Learning Approach in Forex Trading Using Moving Average indicators.” International Journal of Scientific Research, Vol.7, Issue.3, pp.15-21, E-ISSN: 2320, June 2019.

[13] “What is XGBoost Algorithm – Applied Machine Learning”, DataFlair Team• Published February 1, 2018 • Updated November 16, 2018.

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Published

2019-08-31
CITATION
DOI: 10.26438/ijcse/v7i8.356360
Published: 2019-08-31

How to Cite

[1]
A. M. Joshi and S. Prabhune, “Twitter Sentiment Analysis using XGBoost and Logistic Regression: A Hybrid Approach”, Int. J. Comp. Sci. Eng., vol. 7, no. 8, pp. 356–360, Aug. 2019.

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Section

Research Article