Exploration of Opinion from Twitter Data
Nilesh Korde1, Gaurav Kawade2, Sunita Rawat3, Kavita Kalambe4, Abhijeet Thakare5
1Nilesh Korde, Assistant Professor in Computer Science and Engineering Department at Shri Ramdeobaba College of Engineering and Management Nagpur (An Autonomous Institute) , Nagpur, India.
2Gaurav Kawade, Assistant Professor at Shri Ramdeobaba College of Engineering and Management, Nagpur, India.
3Sunita Rawat, Assistant professor in Computer Science and Engineering Department, Nagpur, India.
4Kavita Kalambe, Assistant Professor in the Department of CSE at Shri Ramdeobaba College Of Engineering And Management – [RCOEM], Nagpur, India.
5Abhijeet Ramesh Thakare, Ph.D. from Visvesvarya National Institute of Technology, Nagpur, India in Computer Science and Engineering and his M.E. from Walchand College of Engineering, Sangli, India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 9-12 | Volume-8 Issue-4, November 2019. | Retrieval Number: B3854078219/2019©BEIESP | DOI: 10.35940/ijrte.B3854.118419
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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: To share information nowadays, people use social media sites from all around the world. For example, Twitter is a social media site enable users with facilities like reading, sending post recognized as ‘tweets’ and interrelate with diverse peoples. People often post their sentiments about their day-to-day lives, the whole thing for example places and brands. Businesses makes profit from this vast social media site by gathering data related to sentiments of people. Presenting a model that can accomplish opinion analysis on collected data from Twitter is the aim of this paper. To analyze highly unstructured and unorganized data in Twitter makes it difficult to manage and use. In our proposed model we are combining the work of unsupervised and supervised algorithms. Extracting and classifying each tweet depending on its opinion considered to be a neutral, positive or negative. Zomato and Swiggy are the two subjects about which data were collected to show which online food delivery business has more popularity. We have used diverse machine learning algorithms for testing. Testing metrics like f-score and cross validation were used for testing the result from these models. Our model has shown performance which is considered to be robust on directly mining Twitter texts.
Keywords: Opinion Analysis, Machine Learning, Twitter, Social Media.
Scope of the Article: Machine Learning.