Text Polarity Detection using Multiple Supervised Machine Learning Algorithms
Sagnik Kar1, Mousumi Saha2, Tamasree Biswas3

1Sagnik Kar*, Information Technology, Narula Institute of Technology, Kolkata, India.
2Mousumi Saha, Computer Science & Engineering, Narula Institute of Technology, Kolkata, India.
3Tamasree Biswas, Information Technology, Narula Institute of Technology, Kolkata, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 1612-1618 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8449019320/2020©BEIESP | DOI: 10.35940/ijitee.C8449.019320
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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 is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst. 
Keywords: Accuracy Analysis, Accuracy Comparison, Natural Language Processing, Semantic Orientation, Sentiment Analysis, Twitter Data Analysis
Scope of the Article:  Natural Language Processing