Opinion Mining using Machine Learning Techniques
Nirmal Godara1, Sanjeev Kumar2
1Nirmal Godara, Ph.D Scholar, Department of Computer Science and Engineering, Guru Jambheshwar University of Science and Technology Hisar (Haryana) India.
2Sanjeev Kumar, Assosiate Professor, Department of Computer Science and Engineering,Guru Jambheshwar University of Science and Technology Hisar (Haryana) India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 4287-4292 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4108129219/2019©BEIESP | DOI: 10.35940/ijeat.B4108.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment analysis or opinion mining has gained much attention in recent years.With the constantly evolving social networks and internet marketing sites, reviews and blogs have been obtained among them, they act as an significant source for future analysis and better decision making. These reviews are naturally unstructured and thus require pre processing and further classification to gain the significant information for future use. These reviews and blogs can be of different types such as positive, negative and neutral . Supervised machine learning techniquess help to classify these reviews. In this paper five machine learning algorithms (K-Nearest Neighbors (KNN), Decision Tree, Artificial neural networks (ANNs), Naïve bayes and Support Vector Machine (SVM))are used for classification of sentiments. These algorithms are analyzed usingTwitter dataset. Performance analysis of these algorithms are done by using various performance measures such as Accuracy, precision, recall and F-measure. The evaluation of these techniques on Twitter datasetshowed predictive ability of Machine Learning in opinion mining.
Keywords: Sentiment Analysis, KNN, Decision Tree , Artificial neural networks (ANNs), Naïve bayes and SVM.