Cross domain sentiment analysis using transfer learning

Transfer learning is an emerging research area which extracts knowledge from one or more than one source domains and utilizes this gained knowledge to perform some task in a target domain. It has emerged as a popular topic in recent years, because this technique is considered to be helpful in reducing the cost of labeling. It has many applications on different domains such as Natural Language Processing, Image and Video Processing, etc. The aim is to study transfer learning and implement it for Sentiment Analysis of Tweets by using the knowledge of Yelp reviews. We find that transfer Learning approach is faster than the conventional machine learning approach and give comparable accuracy at much smaller dataset.