Research On Sentiment Analysis Technology Of Financial Text Based On Deep Learning

In recent years,with the continuous improvement of China’s national economic level,more and more people have begun to pay attention to fund management.Compared with the regular demand savings of banks,stocks,funds and other investment and wealth management methods that are easy to operate,have quick returns,and have strong liquidity have gradually entered the public eye,and more and more people have begun to tend to invest their disposable liquidity in stocks.market,and realize the preservation and appreciation of wealth itself.However,the high rate of return of stock investment is often accompanied by a very high risk factor.Stock investors need to always pay attention to stock market information,avoid risks,and seize investment opportunities to maximize personal investment returns.With the passage of time and the development of information technology,with the advantages of fast updating speed and wide dissemination of Internet information,the public has gradually experienced the speed and convenience of obtaining information from the Internet.Investors are also more inclined to obtain and share financial and financial information or investment experience on major online platforms.These text messages often imply emotional tendencies towards the development of the stock market.Emotional information in financial texts affects investors’ psychology to a certain extent,interferes with investment decisions,and then affects the development of the stock market.Therefore,at this stage,in the field of stock financial market analysis,it is more inclined to use natural language processing technology to introduce emotional reference factors of text based on traditional digital indicators for auxiliary modeling.Taking this as the starting point,this thesis analyzes Chinese financial texts and mainly completes the following tasks:Collecting text information from major financial websites,and preliminarily marking financial texts with emotional tendencies,a dataset of sentiment tendencies classification in the field of Chinese financial texts is constructed.Based on the noisy dataset,a multimodel classification combining ensemble learning and confidence learning is proposed.The training process of the machine is used to realize the judgment of the emotional tendency of financial texts and optimize the original data set.Carry out in-depth research on financial text analysis requirements,take financial text sentiment analysis as the main task,and introduce auxiliary tasks of entity recognition.In addition to traditional named entities,emotional word entities are defined,and sentiment analysis and named entity recognition are two natural languages.The correlation between basic tasks is introduced,and the multi-task learning model based on Attention-Seq2 Seq is adopted to realize the extraction of named entity information that investors pay attention to,such as stocks and indices mentioned in financial texts,while judging text sentiment.On the basis of the previous two works,a financial text sentiment quantification system is built,which realizes the functions of financial text collection,financial hot word discovery and word cloud visualization,financial sentiment analysis and quantification.