| dc.description.abstract | Nowadays, information acquisition has become diverse and chaotic. With the improvement of computing power in both hardware and software, as well as the development of various
related application technologies, handling large amounts of data extraction and analysis has become much easier, leading to increased attention to data analysis-related fields and
research.
To deal with this unstructured information, certain statistical methods and algorithms (such as the Latent Dirichlet Allocation model and text sentiment analysis used in this paper) are employed to quantify text, converting it into meaningful numerical data, which can then be used as important reference information or data for decision-making.
Using the Latent Dirichlet Allocation model to construct a text model, the proportions of the underlying topics are extracted. Through text sentiment analysis, the sentiment of the text is analyzed based on these topic information, and the results are classified according to the polarity of the text, determining whether the expressed viewpoint is positive, negative, or neutral.
The classification results obtained in this way can be used for different purposes depending on the field of the article. For example, if applied in the financial market, it can determine the current trends in finance or the overall sentiment towards the general environment or specific
industries. In the investment field, overcoming information disparity is a continuous challenge. By incorporating financial news into the model, it is hoped that investors can obtain basic information that can help strengthen judgment or make predictions.
This paper will use financial news as the experimental subject. Through web crawling, a large amount of news article content will be accurately extracted. The organized information will then be subjected to Latent Dirichlet Allocation modeling and text sentiment analysis to
extract information from news headlines and content, assigning sentiment levels to them, quantifying them, and finally comparing them with the financial market composite index of the same period to verify whether this experimental method is suitable for financial analysis. | en_US |