Research On Text Mining's Assisted Decision On Digital Currency And Securities Investment

Almost at the same time,with the rise of big data,Alpha Dog defeated Ke Jie and Li Shishi to announce the coming of a new era of artificial intelligence.The next round of industrial revolution that humanity will face in the future must be driven by these two disruptive new technologies.The two interact with each other and complement each other.Artificial intelligence uses big data as the cornerstone,and big data uses artificial intelligence as the engine and ladder.It is like the two legs of the future society,and no one can take a good road without it.These two technologies have revolutionary impacts and changes in various industries.The financial industry is dominated by data,and naturally it cannot escape the impact.This article first briefly introduces the application status of these technologies in financial sub-fields such as bank credit,customer reviews,customer recommendations,health and dental insurance,and then focuses on the use of natural language processing technology combined with mainstream media news hot spots to predict and use the US securities market.Big data and machine learning deep learning technology analysis to achieve faster,time-saving and accurate results than human analysis.This paper focuses on the impact of text data such as news and commentary on capital market asset price fluctuations,and explores from the external intuitive performance and in-depth study of their correlation.Finally,a relatively clear conclusion is obtained,which confirms this method.Has practical significance and effectiveness.In today's era where large amounts of heterogeneous data are easy to store,calculate,mine and analyze,it is particularly important for the asset management industry to make good use of unstructured data.The commonly used tool technologies are mainly natural language processing and machine learning technologies.Natural language processing or text mining is a machine learning process that extracts valuable knowledge that is effective,novel,useful,understandable,and scattered in text files,and uses this knowledge to better organize information.Text mining is an important part of "understanding images and natural language while mining knowledge".Text mining is a field of information mining research based on knowledge discovery of text information.Text mining uses intelligent algorithms(such as neural networks,case-based reasoning and probabilistic reasoning)combined with word processing technology to create a large number of unstructured text data sources(documents,spreadsheets,customer emails,questions),extraction or tagging The relationship between the keyword concept and the text,and classify the documents according to their content to obtain useful knowledge and information.Text mining is an interdisciplinary technology,covering various technical means,such as data mining technology,information extraction,information retrieval,machine learning,natural language processing,computational linguistics,statistical data analysis,linear geometry,probability theory and even graph theory.This is a comprehensive field.Data mining technology itself is a new field of current data technology development,in which the history of text mining research is very short.Traditional information retrieval technology is not enough to process large amounts of data,and text mining becomes more and more important.It can be seen that text mining technology is gradually developing from information extraction and related technical fields into an important research sub-field.Obtain information in a different way from the past,mine keyword groups,calculate the correlation between it and digital currency fluctuations,check the market price trend of the Dow Jones Industrial Index,and find the external and internal logical relationship between them,according to the new text The data accurately predicts the daily rise and fall of the prices of the two assets in order to seize the rapidly changing market opportunities,and timely adopt corresponding investment strategies to achieve higher returns than the market to create more investment value.At the same time,use advanced machine learning technology to model and predict the historical prices in the Dow Jones Industrial Index,and combine the corresponding text data to conduct a comprehensive result vote to obtain more accurate prediction results.This article describes how to use the latest natural language processing technology and deep learning models to extract meaningful information from SEC reports and company stock price changes.To collect more details from the text,you can explore advanced technologies such as more professional word embedding technology,such as Sense2Vec.Text data has become a typical representative of unstructured data in the field of asset management(digital currency,securities investment,etc.),and has shown great value in the actual investment process.It is often used for fundamental and technical analysis.The importance of external quantitative analysis technology in investment has been widely recognized by mainstream investment institutions and professionals around the world.Research and exploration in related fields certainly have a bright future and a bright future.But at present,there are still some areas that need to be improved and improved in this aspect of academic theory and industry practice.For example,this paper applies modern keyword extraction and sentiment analysis techniques to accurately summarize text sentiment trends.At the same time,the relationship between sentiment trends and stock price volatility is somewhat simple.In future research,more effective and more accurate emotion recognition algorithms will appear to extract the information required by the text more accurately,while minimizing the impact of noisy data on data model work,standardization and consistency,and both The relationship can be more reasonable.