With the rise of deep learning in the past decade, AI has made breakthroughs in many application fields, such as natural language processing and machine vision. Sentiment analysis is an important research topic in the field of natural language processing. In recent years, more and more research works are actively exploring the use of deep learning to solve the problem of text sentiment analysis. Two major factors have made this research direction a focus: on the one hand, the comprehensive popularity of the internet and the rapid development of social media (e.g., Twitter, blogs, wikis, BBS, social networking, content, community, etc.). As a result, human society has accumulated an unprecedented amount of text data—a text data "ocean"; on the other hand, breakthroughs in deep learning technology provide powerful and effective tools for large-scale automated processing of unstructured data, including text, which also makes it possible to efficiently explore the ocean of text information and extract relevant knowledge from it.
In addition to traditional structured data, the economic and financial fields have also generated a large amount of relevant text information which, together with the development of text analysis technology, has a huge influence on academic research as well as on industrial applications in economics and finance. Unsurprisingly, text mining using deep learning has become a research hotspot in the field of machine learning and fintech.
