Sentiment Analysis manifests in various forms, each serving different analytical purposes:
- Polarity Detection: This fundamental form of Sentiment Analysis classifies content based on its overall tone as positive, negative, or neutral. Polarity detection is widely used in social media monitoring, customer reviews, and opinion mining.
- Emotion Detection: Unlike polarity detection, emotion detection identifies specific emotions conveyed in the text, such as happiness, anger, sadness, or surprise. It often requires more complex models to distinguish between similar emotions.
- Aspect-Based Sentiment Analysis: This approach delves deeper into the text by analysing sentiment for specific aspects or features of a product, service, or topic. For example, in a restaurant review, sentiment towards “food”, “service”, and “ambience” can be independently analysed.
- Intent Analysis: Intent Analysis determines the underlying intention or goal of the speaker or writer. It goes beyond just emotion or sentiment to understand what the speaker hopes to achieve, such as making a purchase, complaining, or requesting information.
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