Named entity recognition (NER) is a subfield of artificial intelligence (AI) and a natural language processing (NLP) technique. It identifies, tags and categorizes named entities in data such as cities, celebrities, brands, etc. It also recognizes and categorizes the type of noun an entity represents such as geography, person or business, which helps in topic clustering.
With NER, a machine learning model can identify differently written or misspelt words so they are not excluded during tagging. For example, NER helps a social listening software identify that Faceb00k and FB both refer to Facebook and are tagged as a social network.
NER algorithms use statistical models to understand words semantically and contextually. Knowledge graphs further establish the relationship between entities and provide a holistic understanding of the data. This capability makes NER critical in sentiment analysis.
When sentiment analysis algorithms calculate sentiment in voice of customer (VoC) data, they are able to assign a sentiment value to each entity identified by NER. These actionable insights help brands make targeted improvements to their strategies such as developing engaging content, streamlining customer care responses, creating better-targeted ads and more.