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Automated Real Time Sentiment Classification of Twitter Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The growth in the area of sentiment analysis and opinion mining has been quick and it also aims to explore the text or opinions on different social media through various machine-learning techniques with the sentiment, confidence, polarity calculations or subjectivity analysis. Users generally tend to express their real feelings on social sites such as Twitter, Facebook, Instagram etc on interesting topics such as brands, products and celebrities etc. But despite the use of various machine learning techniques, there is a dire need of a state-of-the-art approach. This paper tries to contribute to solving these challenges and the creation of an automated system is proposed which reduces the manual labour of preprocessing and filtering of data and the time gap. It tries to integrates various algorithms and results in a module which can be used to determine the sentiment of any text.

Original languageEnglish
Title of host publication3rd International Conference and Workshops on Recent Advances and Innovations in Engineering, ICRAIE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538645253
DOIs
Publication statusPublished - 2 Jul 2018
Externally publishedYes
Event3rd International Conference and Workshops on Recent Advances and Innovations in Engineering, ICRAIE 2018 - Jaipur, India
Duration: 22 Nov 201825 Nov 2018

Publication series

Name3rd International Conference and Workshops on Recent Advances and Innovations in Engineering, ICRAIE 2018

Conference

Conference3rd International Conference and Workshops on Recent Advances and Innovations in Engineering, ICRAIE 2018
Country/TerritoryIndia
CityJaipur
Period22/11/1825/11/18

Keywords

  • API
  • NLTK
  • Pickling
  • Sentiment Analysis
  • Text Classifier
  • Tweepy

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment
  • Computer Networks and Communications
  • Instrumentation
  • Energy Engineering and Power Technology

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