Now that we have a basic understanding of what Sentiment Analysis is, let’s explore how Sentiment Analysis in NLP works. Emotion Detection identifies where emotions, such as happy, angry, satisfied, and thrilled, are detected in a text for analysis. Intention Analysis identifies where intents, such as opinion, feedback, and complaint, etc., are detected in a text for analysis. Intention Analysis and Emotion Detection act similarly to Sentiment Analysis and help round out the basic building blocks of NLP text classification. Sentiment Analysis can also be used in ASR applications, like on speech segments in an audio or video file that is transcribed with a Speech-to-Text API. These ascribed sentiments can then be used to analyze customer feelings and feedback, acting as market research to inform campaigns, products, training, hiring decisions, and KPIs. Often, this means product teams build tools that use Sentiment Analysis to analyze comments on a news article or online reviews of a brand, product, or service, or applied to social media posts, phone calls, interviews, and more. ![]() Sentiment Analysis is used to determine the overall sentiment a writer or speaker has toward an object or idea. Sentiment Analysis is sometimes referred to as Sentiment “Mining” because one is identifying and extracting-or mining-subjective information in the source material. ![]() In Natural Language Processing (NLP), Sentiment Analysis refers to using Artificial Intelligence (AI) and Machine Learning (ML) algorithms to automatically detect and label sentiments in a body of text for textual classification and analysis. In this post, we’ll look more closely at what Sentiment Analysis is, how Sentiment Analysis works, current models, use cases, the best APIs to use when performing Sentiment Analysis, and some of its current limitations. Interested in building tools that intelligently tracking how interviewees feel about certain topics? Or tools that monitor how customers feel toward a new product across all social media mentions? Or that analyze how callers feel about interactions with a particular agent? Sentiment Analysis, powered by advanced AI models, can help.
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