What are some of the best practices and pitfalls of using discriminative models for sentiment analysis?
Sentiment analysis is the task of identifying and extracting the emotional tone and attitude of a text, such as positive, negative, or neutral. It is widely used in natural language processing (NLP) applications, such as social media analysis, customer feedback, product reviews, and opinion mining. One of the common approaches to sentiment analysis is to use discriminative models, which learn to directly map the input features (such as words or sentences) to the output labels (such as sentiment classes) using supervised learning. However, discriminative models also have some challenges and limitations that need to be addressed. In this article, we will discuss some of the best practices and pitfalls of using discriminative models for sentiment analysis, and how to overcome them.