Abstract: Sentiment analysis in text is an important research field for intelligent multimedia understanding. The aim of sentiment classification is to predict the sentiment polarity of opinionated text, which is the core of sentiment analysis. With rapid growth of online opinionated content, the traditional approaches such as lexicon-based methods and classic machine learning methods cannot well handle large-scale sentiment classification problems. In recent years, deep learning has achieved good performance on the intelligent understanding of large-scale text data and has attracted a lot of attention. More and more researchers start to address text classification problems with deep learning. The content of this survey is organized as two parts. We firstly summarize the traditional approaches including lexicon-based methods, machine learning based methods, hybrid methods, methods based on weakly labeled data and deep learning based methods. Secondly, we introduce our proposed weakly-supervised deep learning framework to deal with the defects of the previous approaches. Moreover, we briefly summarize the research work on the extraction of opinion aspects. Finally, we discuss the challenges and future work on sentiment classification.