Machine Learning based Twitter Sentimental Analysis in Business Field
Rohit Ningappa Padti1, Shashank H G2, Syed Azam H S3, Vignesh Pai4, Ramesh B5
1Dr. Ramesh B, Professor, Department of CSE, Malnad College of Engineering, Hassan (Karnataka), India.
2Rohit Ningappa Padti, Department of CSE, Malnad College of Engineering, Hassan (Karnataka), India.
3Shashank H G, Department of CSE, Malnad College of Engineering, Hassan (Karnataka), India.
4Syed Azam H S, Department of CSE, Malnad College of Engineering, Hassan (Karnataka), India.
5Vignesh Pai, Department of CSE, Malnad College of Engineering, Hassan (Karnataka), India.
Manuscript received on 05 December 2019 | Revised Manuscript received on 13 December 2019 | Manuscript Published on 31 December 2019 | PP: 390-394 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10331292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1033.1292S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Social networking sites like twitter have millions of people share their thoughts day by day as tweets. This paper addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users, out of which 100 million are active users and half of them log on twitter on a daily basis i- generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analyzing the sentiments expressed in the tweets. Analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.
Keywords: Sentiment Analysis, Micro-blogging, socioeconomic.
Scope of the Article: Machine Learning