Abstract
Cross-domain sentiment classification initiated with Polarity Detection Task
Highlights
The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually
We are initiating the step towards the Cross Domain Sentiment Analysis (CDSA) task where the manual labeling of documents is not needed but only the text data is required that may be in the form of reviews or tweets
It can be concluded that the proposed method is comparable to traditional learning to perform the CDSC task
Summary
The requirement of the labeled dataset in the source domain makes the Cross Domain Sentiment Classification (CDSC) task complicate in the situation when the dataset is labeled manually. CONCLUSION: The proposed method does not need to manually label the documents in either of the domain (source or target), it overcomes the human intervention and is time saving and cheap process, unlike traditional CDSC tasks. Sentiment analysis analyzes the preferences and opinions of people (given in the form of online reviews or tweets) who have used the services, and tells their orientation, either positive or negative. This analysis for orientation (polarity) prediction can be used to make various decisions for, the users: to use the service or to buy any product, as well as to the service providers: to make decisions based on users’ predilections that can be applied to make recommendation systems.
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