Opinion Mining on Twitter Data Using Supervised Machine Learning Algorithms
DOI:
https://doi.org/10.26438/ijcse/v6si6.6366Keywords:
Sentiment Analysis, Naive Bayes Classifier, SVM, Random Forest Classifier, KNN ClassifierAbstract
The emerging digital era generates heaps of computerized information. The greater part of the electronic data in the world today has been created over the last recent couple of years. The velocity of data generation is unimaginable and incomprehensible. People nowadays are commonly using the digital media to express their stand point about a topic. These opinions are analyzed automatically to know whether the client remark is ideal or not good to the said theme. This ought to be possible by Opinion Mining, also called as Sentiment Analysis. The basic chore in Sentiment Analysis is to categorize the orientation of a given review and subsequently identifying whether the sentiment implied is positive, negative or fair. In this paper, the tweets based on the news thread “Whether National Anthem is needed at Cinema theatres?” are analyzed based on the user rating for the opinions. The classifiers like Bernoulli and Multinomial Naive Bayes, Random Forest, k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) have been used for analyzing the opinions and found that the Random Forest classifier and Multinomial Naïve Bayes classifier is the top rated classifier based on their accuracy values.
References
[1] Khan, Khairullah, Baharum B. Baharudin, and Aurangzeb Khan. "Mining opinion targets from text documents: A review." Journal of Emerging Technologies in Web Intelligence 5.4 (2013): 343-353.
[2] Gupte, Amit, Sourabh Joshi, Pratik Gadgul, Akshay Kadam, and A. Gupte, "Comparative study of classification algorithms used in sentiment analysis.", International Journal of Computer Science and Information Technologies , Vol. 5 (5) : 6261-6264, 2014
[3] Gupta, Ankita, Jyotika Pruthi, and Neha Sahu. "Sentiment Analysis of Tweets using Machine Learning Approach." (2017).
[4] Dey, Lopamudra, Sanjay Chakraborty, Anuraag Biswas, Beepa Bose, and Sweta Tiwari. "Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier." arXiv preprint arXiv:1610.09982 (2016).
[5] Liu, Bing. "Sentiment analysis and opinion mining." Synthesis lectures on human language technologies 5, no. 1 (2012): 1-167.
[6] Liu, Bing, and Lei Zhang. "A survey of opinion mining and sentiment analysis." In Mining text data, pp. 415-463. Springer US, 2012.
[7] Mohammad, Saif M. "Sentiment analysis: Detecting valence, emotions, and other affectual states from text." In Emotion measurement, pp. 201-237. 2016.
[8] Wagner, Wiebke. "Steven bird, ewan klein and edward loper: Natural language processing with python, analyzing text with the natural language toolkit." Language Resources and Evaluation 44, no. 4 (2010): 421-424.
[9] Palmer, David D. "Tokenisation and sentence segmentation." Handbook of natural language processing (2000): 11-35.
[10] Ye, Qiang, Ziqiong Zhang, and Rob Law. "Sentiment classification of online reviews to travel destinations by supervised machine learning approaches." Expert systems with applications 36.3 (2009): 6527-6535.
[11] Aldoğan, Deniz, and Yusuf Yaslan. "A comparison study on active learning integrated ensemble approaches in sentiment analysis." Computers & Electrical Engineering 57 (2017): 311-323.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
