Sentiment analysis and emotion recognition are two of the hottest topics in speech understanding today. But they're often confused for one another-so much so that people often say "sentiment analysis" when they're referring to emotion recognition. In this post, we'll explain what both sentiment analysis and emotional recognition are, how they are used in business, and some of the limitations and challenges of each.

1. What is Sentiment Analysis?

Sentiment analysis is a typically text-based machine learning classification task. It might operate on single sentences, paragraphs, or even entire articles. The typical goal of sentiment analysis is to determine whether the author of a text has a positive or a negative opinion about whatever the topic of the text is. To this end, the typical training sets for sentiment analysis models are things like IMDb reviews of movies and Amazon product reviews, where it's easy to tell how someone felt about a topic (that is, their star ratings can be used as part of the training data). Sentiment analysis has a variety of uses, including analyzing customer feedback, monitoring social media conversations, tracking brand reputation, gauging public opinion on a topic or issue, and evaluating customer satisfaction levels.

Limitations and Challenges of Sentiment Analysis

There are, of course, limitations to systems like this. Sarcasm, for example, can be hard for sentiment analysis to detect (which isn't surprising since humans also struggle to correctly identify sarcasm in written language). That might be less of a problem when you're training and have the groundtruth of someone's rating, but in the real world, the goal of sentiment analysis is to determine how someone felt in the absence of a rating.

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