The ever increasing volumes of data is one of the biggest challenges enterprises face today. The reason there is so much data is because enterprises have information accumulated from various sources, and in diverse formats. What makes it important is the fact that it holds valuable insights that can be used by the business to make data driven decisions. And what makes analyzing it challenging is the fact that majority of it is unstructured, which makes it impossible to analyze it using the traditional keyword based method.
So, one may ask what is the solution. The solution lies in the new age enterprise search tools like 3RDi Search and Coveo have a text analysis API to analyze the most complex unstructured data to reveal the insights within. In fact, these are the tools an enterprise needs today to manage and organize the diverse forms of data that are collected. What makes such a software a good investment is the fact that it has powerful features built-in that cater to every text analysis and text mining requirements that organizations may face when dealing with enterprise data. Here are the key features that a new age text analysis API offers.
1] Recognition of Named Entities
A very important feature offered by new age text analysis APIs, named entity recognition is also known as entity recognition or entity chunking. It is a process that involves the classification of all the named entities present in the data, such as names of people, places, books, etc., into pre-defined categories. The names entities are extracted and placed under relevant categories. This is a very powerful feature to extract information from unstructured data real fast.
2] Detecting Semantic Similarity
Semantics is the technology involved in deriving meaning of unstructured data. Semantic similarity is a technology that classifies entities in unstructured data on the basis of their meaning, and it’s a very effective method to classify data and helps in quick analysis of unstructured data.
3] Sentiment Analysis
A powerful text analysis feature, sentiment analysis refers to analyzing a piece of content to derive the sentiment or emotion behind it. 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.
4] Natural Language Processing
Natural language processing or NLP is one of the key features required for effective text analysis. NLP helps the software to understand and 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.
5] Extraction of Key Phrases
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.
The Final Word
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.