What is Sentiment Analysis?

Automatic Speech Recognition (ASR) involves detecting sentiment within speech segments of audio or video files, often referred to as "sentiment mining". This is a well-studied field with numerous applications across various industries. In ASR, the goal is to categorize sentiments into positive, negative, or neutral based on a polarity score between -1 and 1. Sentiment Analysis models are typically built using transformers, which have proven effective in dealing with sequential data like text and speech. Applications of sentiment analysis include tracking customer feelings towards products or services, analyzing agent behavior during customer interactions, and determining participant sentiments during virtual meetings. Limitations currently exist in the nuanced categorization of sentiments and the use of audio versus text training data. However, ongoing research aims to improve these aspects, potentially enabling more descriptive sentiment labels and increased accuracy.