What is Sentiment Analysis?
Sentiment analysis is the interpretation and classification of emotions (positive, negative, neutral, etc..) within text data using text analysis techniques. Sentiment analysis allows businesses to identify customer sentiment toward products, brands or services in online conversations, feedback and act proactively.
- Polarity: if the speaker express a positive or negative opinion,
- Subject: the thing that is being talked about
- Opinion holder: the person, or entity that expresses the opinion.
Types of Sentiment Analysis
Sentiment analysis assumes various forms, from models that focus on polarity (positive, negative, neutral) to those that detect feelings and emotions (angry, happy, sad, etc), or even models that identify intentions (e.g. interested v. not interested).
Fine-grained sentiment analysis
Fine-grained sentiment analysis is based on a larger scale of sentiment type and could be used to interpret 5-star ratings in a review for instance.
Emotion detection aims at detecting emotions, like happiness, frustration, anger, sadness, and so on. Many emotion detection systems use lexicons or complex machine learning algorithms.
It can help for example in this text: ``The battery life of this camera is too short``, it would be able to determine that the sentence expresses a negative opinion about the feature battery life.