Discover why Sentiment Analysis is the ideal NLP feature for understanding customer opinions about your brand. Learn its benefits and applications in AI.
Table of Contents
Question
Which Natural Language Processing (NLP) feature is the appropriate one to use to find out what customers think about your brand?
A. Named Entity Recognition (NER)
B. Summarization
C. Sentiment analysis
D. Entity linking
Answer
C. Sentiment analysis
Explanation
Sentiment analysis is the appropriate Natural Language Processing (NLP) feature to use for finding out what customers think about your brand. It focuses on understanding the sentiment (positive, negative, neutral) expressed in text. In this scenario, you want to consider your customers’ opinions about your brand, which essentially translates to their sentiment towards it.
Sentiment analysis can be applied to analyze various types of customer feedback, such as:
- Reviews
- Social media posts
- Customer service interactions
- Survey responses
By analyzing the sentiment in these different forms of text, you can receive an understanding of the customers’ perception about your brand, allowing you to identify areas of improvement and develop strategies to improve customer satisfaction.
Named Entity Recognition (NER) is an NLP feature that identifies and classifies entities such as names, locations, and organizations. While it can identify mentions of a brand name, it does not reveal the sentiment associated with them.
Summarization is an NLP feature that condenses a text into a shorter version while retaining the main points. It does not provide insights into the sentiment expressed in the text.
Entity linking is a feature that connects identified entities to relevant entries in a knowledge base. It does not analyze the sentiment surrounding the entities.
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