Scholar Publishing Group The best way to conference proceedings by Scholar Publishing Group

Scholar Publishing Group

Machine Learning and Text Mining in Investor Sentiment

Download as PDF

DOI: 10.38007/Proceedings.0001955


Tao Wang

Corresponding Author

Tao Wang


Abstract: Investor sentiment, as a core concept in the field of behavioral finance, has been widely studied by scholars. Through scholars' long-term efforts, investor sentiment indexes have undergone a development process from direct-type sentiment indexes to indirect-type sentiment indexes. Although sentiment indices are currently able to better portray investor sentiment, they also have some shortcomings. This paper focuses on how machine learning and text mining techniques can identify investor sentiment. Firstly, it introduces how to transform text into data that can be used for computer analysis through methods such as word separation and vectorization, followed by how to use machine learning methods to mine investor sentiment. A large amount of practice shows that machine learning methods are better applied in the field of investor sentiment.


Keywords: Behavioral Finance; Sentiment Indexes; Machine Learning; Text Mining