Sports, Americas, Special Report

[AAinnonna]

Using this technology, it is possible for the text analysis API to "understand" whether the piece of text depicts a sad or happy emotion. One of the key applications of sentiment analysis is the analysis of data derived from social media. This data can be analyzed to successfully understand what the users think about the brand or the product. Natural language processing or NLP is one of the key features required for effective text analysis. NLP helps the software to understand. Process the natural language spoken by human beings. Without NLP technology, users are required to interact with machines through a language understood by machines. However, NLP can make it possible for humans to interact with machines directly and take inputs and provide outputs in the human language. Home assistant devices like Google Home are a good example of the application of NLP. Another important feature of the text analysis tool is extraction of key phrases. There was a mention of named entity extraction in the previous section of the article, and here it is about extraction of key phrases. This technology is really useful in getting an idea about the theme or subject of the content, and you find it in every new age enterprise search tool. That was about the most important features of a text analysis API. In addition to those mentioned here, there are a lot of other features that text analysis tools would provide. However, which software to choose depends entirely on the requirements of the organization.




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