Cross-Domain Sentiment Classification Using SST

Authors

  • PK Megha Royal College of Engineering and Technology, Thrissur, Kerala
  • Hima KG Royal College of Engineering and Technology, Thrissur, Kerala

Keywords:

Sentiment Analysis, SST

Abstract

Sentiment analysis refers to the use of natural language processing and machine learning techniques to identify and extract subjective information in a source material like product reviews. Due to revolutionary development in web technology and social media reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Across domain sentiment analysis invokes adaptation of learned information of some (labeled) source domain to unlabelled target domain. The method proposed in this project uses an automatically created sentiment sensitive thesaurus(SST) for domain adaptation. Based on the survey conducted on related literature, we identified L1 regularized logistic regression is a good binary classifier for our area of interest.This makes our project more accurate in sentiment classification.We can use this as an application for product reviews. In addition to the previous work we propose the use of senti wordnet and adjective adverb combinations for those effective feature learning.

References

Andrea Esuli , Fabrizio Sebastiani, ” SENTIWORDNET: A Pub-licly Available Lexical Resource for Opinion Mining”, Proc. of the 5th Conf. on Language Resources and Evaluation (LREC06), 2006.

Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan.”Thumbs up? sentiment classification using machine learning techniques”. In EMNLP 2002, pages 79-86.

Danushka Bolegalla ,David Weir, John Caroll ”Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification”.

John Blitzer, Ryan McDonald, and Fernando Pereira.”Domain adaptation with structural correspondence learning”. In EMNLP 2006.

Farah Benamara, Sabatier Irit, Carmine Cesarano, Napoli Fed-erico, Diego Reforgiato, ” Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone ”, In Proc of Int Conf on Weblogs and Social Media , 2007.

Downloads

Published

2025-11-11

How to Cite

[1]
P. Megha and K. Hima, “Cross-Domain Sentiment Classification Using SST”, Int. J. Comp. Sci. Eng., vol. 5, no. 6, pp. 249–252, Nov. 2025.

Issue

Vol. 5 No. 6 (2017): IJCSE June Edition

Section

Review Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.