Sentiment Analysis on Social Media Big Data with Multiple Tweet Words
S. Uma Maheswari1, S. S. Dhenakaran2
1S. Uma Maheswari Department of Computer Science, Alagappa University. (Tamil Nadu), India.
2Dr. S. S. Dhenakaran Department of Computer Science, Alagappa University. (Tamil Nadu), India.
Manuscript received on 03 August 2019 | Revised Manuscript received on 09 August 2019 | Manuscript published on 30 August 2019 | PP: 3429-3434 | Volume-8 Issue-10, August 2019 | Retrieval Number: J96840881019/19©BEIESP | DOI: 10.35940/ijitee.J9684.0881019
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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: The main objective of this paper is Analyze the reviews of Social Media Big Data of E-Commerce product’s. And provides helpful result to online shopping customers about the product quality and also provides helpful decision making idea to the business about the customer’s mostly liking and buying products. This covers all features or opinion words, like capitalized words, sequence of repeated letters, emoji, slang words, exclamatory words, intensifiers, modifiers, conjunction words and negation words etc available in tweets. The existing work has considered only two or three features to perform Sentiment Analysis with the machine learning technique Natural Language Processing (NLP). In this proposed work familiar Machine Learning classification models namely Multinomial Naïve Bayes, Support Vector Machine, Decision Tree Classifier, and, Random Forest Classifier are used for sentiment classification. The sentiment classification is used as a decision support system for the customers and also for the business.
Keywords: Opinion Mining, Social Media, Big Data, Support Vector Machine, NLP.
Scope of the Article: Big Data