Sentiment analysis of text with lossless mining

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Downloads (Pure)

Abstract

Social networks are becoming more and more real with their power to influence public opinions, election outcomes, or the creation of an artificial surge in demand or supply. The continuous stream of information is valuable, but it comes with a big data problem. The question is how to mine social text at a large scale and execute machine learning algorithms to create predictive models or historical views of previous trends. This paper introduces a cyber dictionary for every user, which contains only words used in tweets - as a case study. Then, it mines all the known and unknown words by their frequency, which provides the analytic capability to run a multi-level classifier.
Original languageEnglish
Title of host publicationInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2021)
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages5
ISBN (Electronic)9781665412629
ISBN (Print)9781665429436
DOIs
Publication statusPublished - 10 Nov 2021
EventInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering - hybrid conference, Mauritius
Duration: 7 Oct 20218 Oct 2021
http://www.iceccme.com/

Conference

ConferenceInternational Conference on Electrical, Computer, Communications and Mechatronics Engineering
Abbreviated titleICECCME 2021
Country/TerritoryMauritius
Period7/10/218/10/21
Internet address

Keywords

  • Losless mining
  • Sentiment analysis
  • Social media analytics
  • Social network

Cite this