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, contributing to topic clustering.
With NER, a machine learning model can identify alternatively written or misspelled words to prevent their exclusion during tagging. For instance, 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 strengthen the relationship between entities and provide a comprehensive understanding of the data. This capability makes NER key 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 creating more engaging content, streamlining customer care responses, creating better-targeted ads and more.