5 Key Features of Text Analysis API

Enterprises today face the growing challenge of dealing with ever increasing volumes of data. The data comes from various sources and holds hidden insights that can help the business frame strategic decisions to fuel business growth. However, the challenge is that a lion’s share of this data is unstructured, which calls for advanced text analysis tools.

The new age enterprise search software platforms have emerged as the solution to the problem faced by enterprises when it comes to analysis and management of large volumes of data. Platforms like 3RDi Search, Coveo and Commvault have powerful features that have been inculcated to redefine the way enterprises look at search. These powerful platforms offer advanced text mining functionalities that make data analysis a simplified and quick process that brings great results. Every enterprise search tool has a text analysis API at its core that powers it. This article takes a quick look into the 5 key features of a text analysis API.

1] Sentiment Analysis

Sentiment analysis is a very useful feature that is highly effective for analyzing unstructured data. It implies analyzing a chunk of unstructured data to determine the sentiment behind it. In other words, with sentiment analysis, the text analysis API is capable of determining the sentiment or emotion of the writer of the content. THIs feature is extremely useful for enterprises to analyze social media data to find out what users in general feel about their brand or product.

2] Natural Language Processing

An essential feature of text analysis APIs, Natural Language Processing (NLP) is a technology that makes it possible for the device to understand and process human language and even present the results or output to the user in the language spoken by him. NLP is the technology that has redefined the way users interact with search platforms. Even Google Home and the Amazon Echo have NLP as one of the driving technologies.

3] Semantic Similarity

Semantic similarity is based on the semantic search technology which is all about deriving meaning from unstructured data. Semantic similarity is the process of extracting content that is similar in meaning from a chunk of unstructured data. It is useful for effective classification of content in unstructured data for easy analysis.

4] Named Entity Recognition

Also known as entity identification or entity chunking, named entity recognition is all about extracting and classifying named entities in unstructured data into pre-defined categories, such as geographical locations, name of individuals, email addresses, etc. It is a highly effective method to extract the most relevant information from unstructured data accurately and in a short time.

5] Key Phrase Extraction

Another key feature of a text analysis API, key phrase extraction involves extraction of key phrases from a chunk of unstructured data in order to get a summary of the key points covered in the data. This can be a really quick way to determine what the content is all about and is a feature that users of enterprise search platforms find really useful.

In addition to the key features listed above, a text analysis API can have many features to cater to different requirements in text and data analysis. However, when choosing a text analysis API, one must ensure that these features are present. In addition to these features, enterprises may look for other features that can cater to the specific requirements of their project.