Natural Language Processing in Finance
Today, Machine Learning (ML) is being used in almost every industry. Its use has not only helped the businesses to emerge, but have also helped them in blooming. One such tool that is being actively used in businesses is Natural Language Processing (NLP).
Natural Language Processing (NLP) is the component of Artificial Intelligence (AI) that deals with the interpretation of human language by machines, as spoken. An application of NLP that is used widely is Sentiment Analysis.
Sentiment analysis is the process of understanding an opinion about a given subject from written or spoken language. In a world where we generate trillions bytes of data every day, sentiment analysis has become a key tool for making sense of that data.
It is a tool that builds systems that try to identify and extract opinions within text by analyzing:
- Polarity: if the expression is a positive or negative opinion,
- Subject: the thing that is being talked about.
Sentiment Analysis is commonly used in finance industry, where with the help of the system one can easily track the movement of the market. Our financial market analysts make predictions on the stock market based on opinions and happenings in the news. Similarly, Sentiment Analysis is making it possible for computers to do the same job now and with higher efficiency as computers have the capability to scan through huge chunk of text across various news channels within seconds.
A simple example of the real application of Sentiment Analysis for the financial sector can be explained by the task of assigning positive, negative or neutral sentiment values to the words. For instance, words such as “good”, “profit”, “benefit”, “positive”, and “growth” are all tagged with positive values while words such as “risk”, “fall”, “bankruptcy”, and “loss” are tagged with negative values. By predicting the sentiments and values of the news, the system analyses upward and downward movements of the financial market.
By:- Kunal Singh