It is well known that sentiments can move the stock markets. Discovering the price point for a given stock (or index) using financial text sentiment is a fundamental problem in finance.
The current state of the art sentiment analysis in finance is based on the standard frequency of words in the text technique. However, this technique misses the semantic meaning of the sentence. For example, a tweet such as: apple gains over samsung! has a positive sentiment, because of bi-gram (gains over). Simlarly, samsung gains over apple! also has a positive sentiment. However, one is unable to distinguish, which company has a positive sentiment over other using the current technique. This is because the meaning/semantics of the sentence is unknown.
Generative AI techniques (e.g., ChatGPT) encode the sentiment of the sentence. The AIM of the project is to use generative AI engines to perform sentiment analysis of textual data (structured from news) and unstructured from tweets to build a semantic preserving sentiment stream.
Once successful, we would like to understand a number of fundamental factors about sentiment and their impact on market price. Such as: (1) how much positive sentiment affects the market price, (2) Is the affect transient of permanent, etc.
Undergraduate
A generative AI engine for semantics preserving sentiment anlaysis of financial text.
Should be interested in markets and statistics/ML learning.
Embedded Systems (405.760, Lab)