Natural language processing (NLP) is an area of linguistics, computer science, and artificial intelligence concerned with how computers interact with human language. Using it, you can extract and categorize meaningful information from any text input with entities and topics detection, respectively.
The term “entity detection” is used to describe the method used to recognize proper nouns in a text. The purpose is to assign these entities to predetermined classes. This can identify things like location, time, names, organization, and anything that was mentioned in the unstructured text.
When applied to unstructured text data, the term “topic detection” describes the method by which certain themes or subjects are extracted automatically. Its purpose is to understand the text and extract the most important points for further review. This is a very helpful activity in text mining. Insights must be learned from massive amounts of unstructured text data.
When and why would you utilize a platform that detects entities and topics?
Entity and topic detection are two of the most important tasks in NLP. They are employed in many different industries such as logistics, real estate, finance, banking, insurance, and professional services. To find out how customers feel about brands, services, and products, entity detection can be employed. It vastly improves the speed and quality at which insights can be detected. Similarly, you may use topic detection to pull out the most important themes for your company.
How do entity and topic detection work in feedback management solutions?
Entity and topic detection are two natural language processing methods that can be used for end-customer feedback. They both assess and glean helpful information about customer journeys.
When analyzing customer feedback, entity detection can help pick out particular items, services, or people referenced. The text in the feedback can be utilized to identify what parts of the experience are being reviewed and classify them. If you want to know how customers feel about a certain product, you may utilize entity detection on product mentions.
Use case Example
We’ll use Company A as an example, a real estate firm that is very interested in hearing feedback from its end customers (tenants; buyers) so it may continue to improve the services it provides. In this case, the company will want to look at topics such as maintenance, security, and property visits. They will be able to create themes based on feedback data that will automatically be grouped into categories for insightful analysis.
They use Feedier to centralize their feedback data on a single platform. Then do analyses on it with the BX intelligence module. Feedier’s proprietary technology uses natural language processing (NLP) to evaluate all feedback and then presents the findings in an easily digestible format that can be used to pinpoint problems and drive strategic decisions.
You can see in this image different feedback gathered through different channels that are directly categorized into entities and/or topics.
However, topic identification may be utilized to extract the recurrent ideas expressed by customers. This data may be utilized to distill the feedback’s key points and provide a bird’s-eye view of the customer’s overall satisfaction. By using topic detection, a company may learn about the most often mentioned consumer complaints and where they might make improvements.
End customer feedback data can be analyzed and insights extracted using the potent NLP methods of entity and topic detection. By using these strategies, companies will learn more about their customers’ perspectives, experiences, and expectations, at scale, which in turn boosts customer satisfaction and loyalty.