NLP Applications#
Textual analysis#
Textual analysis has gained significant popularity in recent years, especially in the field of asset pricing, macroeconomics, and other related fields. This approach involves analyzing large volumes of text to extract meaningful insights and information, which can be used to inform decision-making.
One of the primary reasons for the growing popularity of textual analysis is that it enables researchers to measure economic concepts that are otherwise hard or impossible to measure. For example, sentiments, emotions, and attitudes can be extracted from social media posts, news articles, and other textual data to provide insights into consumer behavior, market trends, and other economic indicators.
Interestingly, the simplest applications of textual analysis have proven to be the most successful so far. Many cutting-edge methods of machine learning, such as deep learning and neural networks, are not always necessary and can even be counter-productive, similar to kitchen-sink regressions that are prone to over-fitting.
Therefore, the advice for researchers interested in textual analysis is to keep it simple and stay close to the text. This means reading a lot of relevant texts and using basic techniques to extract insights. The frontier of this field is more in learning from new data rather than using fancy techniques.
Textual analysis is increasingly popular in asset pricing, macroeconomics, and other fields.
It enables researchers to measure economic concepts that are otherwise hard or impossible to measure.
Simple applications of textual analysis have been the most successful so far.
Many cutting-edge methods of machine learning are not necessary and can even be counter-productive.
The advice is to keep it simple, stay close to the text, and read a lot.
The frontier of the field is in learning from new data rather than using fancy techniques.