The social network growth is increased and the interest of people in analyzing reviews and opinions for products before buy them. In this regarding research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. Analyzing the sentiments in massive user-generated online data, such as product reviews and micro blogs, has become a hot research topic. Sentiment analysis is widely known as a domain dependent problem. In this paper presents a rigorous survey on cross domain sentiment analysis, challenges for social media, then identified problems in different domains usually have different sentiment expressions and a general sentiment classifier is not suitable for all domains. The main problem is the selection of sentiment from huge volume of opinionated data for different kinds of event which is available in the social networks, but there exist a huge difficulty in predicting the accurate outcome of the event at cross domain. A natural solution to this problem is to train a domain-specific sentiment classifier for each target domain. However, the labeled data in target domain is usually insufficient, and it is costly and time-consuming to annotate enough samples.

Data Mining, Opinion Mining, Machine Learning, Cross - Domain Sentiment Analysis, SentiWordNet

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