Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings

Abstract

Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings Unsupervised Cross-domain Sentiment Classification is the task of adapting a sentiment classifier trained on a particular domain (source domain), to a different domain (target domain), without requiring any labeled data for the target domain. By adapting an existing sentiment classifier to previously unseen target domains, we can avoid the cost for manual data annotation for the target domain.

Conclusion

Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings We considered three constraints that must be satisfied by an embedding that can be used to train a crossdomain sentiment classification method. We evaluated the performance of the individual constraints as well as their combinations using a benchmark dataset for crossdomain sentiment classification.

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