Evaluating Cross Domain Sentiment Analysis using Supervised Machine Learning Techniques

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

6 Citations (Scopus)

Abstract

Sentiment Analysis is the process of computationally identifying and categorizing opinion expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic is negative, positive or neutral. Many researchers have proposed novel methods for sentiment classification especially using supervised machine learning (ML) techniques. However, there is still limited research with successful results in Cross-Domain Sentiment Analysis. Therefore, previous experiments were replicated by using different ML techniques with several enhancements in order to better understand the sentiment classification process and to compare results with cross-domain analysis. Limitations of the proposed approach are discussed and a new automated model is suggested for future work.
Original languageEnglish
Title of host publicationIntelligent Systems Conference 2017
Place of PublicationLondon
PublisherIEEE Explore
Number of pages8
ISBN (Electronic)978-1-5090-6435-9
ISBN (Print)978-1-5090-6436-6
DOIs
Publication statusPublished - 7 Sep 2017
EventSAI Intelligent Systems Conference 2017 (IntelliSys 2017) - America Square Conference Center, London, United Kingdom
Duration: 7 Sep 20178 Sep 2017

Conference

ConferenceSAI Intelligent Systems Conference 2017 (IntelliSys 2017)
CountryUnited Kingdom
CityLondon
Period7/09/178/09/17

Keywords

  • cross-domain sentiment analysis
  • machine learning
  • supervised techniques
  • Lexicon-Based Approach

Fingerprint Dive into the research topics of 'Evaluating Cross Domain Sentiment Analysis using Supervised Machine Learning Techniques'. Together they form a unique fingerprint.

  • Cite this