Cross domain sentiment analysis using transfer learning

Bharat Gupta, Shivam Awasthi, Parshant Singh, Likhama Ram, Pramod Kumar, Bakshi Rohit Prasad, Sonali Agarwal

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

5 Scopus citations

Abstract

Transfer learning is an emerging research area which extracts knowledge from one or more than one source domains and utilizes this gained knowledge to perform some task in a target domain. It has emerged as a popular topic in recent years, because this technique is considered to be helpful in reducing the cost of labeling. It has many applications on different domains such as Natural Language Processing, Image and Video Processing, etc. The aim is to study transfer learning and implement it for Sentiment Analysis of Tweets by using the knowledge of Yelp reviews. We find that transfer Learning approach is faster than the conventional machine learning approach and give comparable accuracy at much smaller dataset.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Industrial and Information Systems, ICIIS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538616741
DOIs
StatePublished - 2 Jul 2017
Externally publishedYes
Event12th IEEE International Conference on Industrial and Information Systems, ICIIS 2017 - Peradeniya, Sri Lanka
Duration: 15 Dec 201716 Dec 2017

Publication series

Name2017 IEEE International Conference on Industrial and Information Systems, ICIIS 2017 - Proceedings
Volume2018-January

Conference

Conference12th IEEE International Conference on Industrial and Information Systems, ICIIS 2017
Country/TerritorySri Lanka
CityPeradeniya
Period15/12/1716/12/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

  • Transfer Learning
  • Twitter
  • Yelp reviews
  • knowledge transfer
  • sentiment analysis

Fingerprint

Dive into the research topics of 'Cross domain sentiment analysis using transfer learning'. Together they form a unique fingerprint.

Cite this