A Survey of Sentiment Analysis Based on Transfer Learning <s> II. THE RELATED METHODS <s> The Web holds valuable, vast, and unstructured information about public opinion. Here, the history, current use, and future of opinion mining and sentiment analysis are discussed, along with relevant techniques and tools. <s> BIB001 </s> A Survey of Sentiment Analysis Based on Transfer Learning <s> II. THE RELATED METHODS <s> Bag-of-words (BOW) is now the most popular way to model text in statistical machine learning approaches in sentiment analysis. However, the performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. We propose a model called dual sentiment analysis (DSA), to address this problem for sentiment classification. We first propose a novel data expansion technique by creating a sentiment-reversed review for each training and test review. On this basis, we propose a dual training algorithm to make use of original and reversed training reviews in pairs for learning a sentiment classifier, and a dual prediction algorithm to classify the test reviews by considering two sides of one review. We also extend the DSA framework from polarity (positive-negative) classification to 3-class (positive-negative-neutral) classification, by taking the neutral reviews into consideration. Finally, we develop a corpus-based method to construct a pseudo-antonym dictionary, which removes DSA's dependency on an external antonym dictionary for review reversion. We conduct a wide range of experiments including two tasks, nine datasets, two antonym dictionaries, three classification algorithms, and two types of features. The results demonstrate the effectiveness of DSA in supervised sentiment classification. <s> BIB002
According to the different text types and application fields, sentiment analysis is also known as evaluation extraction, opinion mining and emotional polarity judgment BIB001 , BIB002 . According to the different principles of sentiment analysis algorithms, this paper divides the methods into three categories, namely traditional methods, deep learning methods, and transfer learning methods to discuss and investigate recent related papers.