Enhancing Accuracy in Cross-Domain Sentiment Classification by Using Discounting Factor

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Provided by: Kongu Engineering College
Topic: Big Data
Format: PDF
Sentiment analysis involves in building a system to collect and examine opinions about the product made in blog posts, comments, reviews or tweets. Automatic classification of sentiment is important for applications such as opinion mining, opinion summarization, contextual advertising and market analysis. Sentiment is expressed differently in different domains and it is costly to annotate data for each new domain. In cross-domain sentiment classification, the features or words that appear in the source domain do not always appear in the target domain. So a classifier trained on one domain might not perform well on a different domain because it fails to learn the sentiment of the unseen words.
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