| Summary: | With the rapid development of the Internet industry, sentiment analysis has grown into one of the popular areas of natural language processing (NLP). Through it, the implicit emotion in the text can be effectively mined, which can help enterprises or organizations to make an effective decision, and the explosive growth of data undoubtedly brings more opportunities and challenges to the sentiment analysis. At the same time, transfer learning has emerged as a new machine learning technique that uses the existing knowledge to solve different domain problems and produces state-of-the-art prediction results. Many scholars apply transfer learning to the field of the sentiment analysis. This survey summarizes the relevant research results of the sentiment analysis in recent years and focuses on the algorithms and applications of transfer learning in the sentiment analysis, and we look forward to the development trend of the sentiment analysis.
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