Cross-Domain Sentiment Classification Using SST - Megha, PK; Hima, KG (International Journal of Computer Sciences and Engineering, 2025)
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.