Sentiment Detection

Sentiment Detection – Short Explanation
Sentiment Detection in the Real World
Sentiment Detection in the World of AI

Sentiment Detection – Short Explanation

Sentiment detection or sentiment analysis is the means through which AI, using natural language processing and other tools, determines the emotions in text or speech. In business, having an understanding of emotion is critical in determining the appropriate actions to take.

Sentiment detection is primarily focused on positive or negative aspects and can be further categorized through emotional statements. Based on how it is configured and used, it can help companies decide if a prospect is interested in a product or service or not very quickly and easily.

Sentiment Detection in the Real World

Sentiment detection and analysis are used regularly in the social media world where it can help researchers understand public opinions on specific topics. Shifts in sentiments are a powerful indicator of the changing views. They have been correlated back to swings in the stock market, so correct analysis can be extremely profitable if done well.

Sentiment detection and analysis is also useful in advertising and market research as well as customer service. By understanding how customers perceive your product and service, different escalation strategies and steps can be automatically applied to help improve customer experience.

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Sentiment Detection in the World of AI

Sentiment detection and analysis is difficult without AI tools and algorithms. For example, the phrase “the battery life is too short” does not by itself indicate positive or negative emotion. In context and through human understanding, it is easy to understand that this is negative, however.

With over 80% of the world’s data in an unstructured form, analyzing the information by hand is an impossible task. By using AI systems, however, the task becomes more straightforward. AI algorithms can tag and label this unstructured data so that companies and businesses understand what actions they need to take.

Sentiment detection and analysis is not easy, though. Human languages are complex and have their own rules with unique grammar structures and cultural connotations that need to be accounted for. For example, the sentence: “I lost the game. Amazing!” could to a machine sound like a positive statement. However, you or I read the phrase understand the sarcastic connotations and tone. The word “Amazing” by itself is generally positive, but in context, the sentence is actually negative.