Text Analytics

How NLP is Automating the complete Text Analysis Process for Enterprises?

In a world where we generate 2.5 quintillions (number of zeros = 18!) bytes of data every day, text analysis has become a key tool for structuring the data and getting the key insights. The organized and insightful data is worth millions of dollars in the present day scenario and it is no secret that   →

Reasons to Replace Dictionary Based Text Mining with Machine Learning Techniques

  More than 80% of data in most organizations is about how the customers are engaging with the product. Monitoring this relationship using text mining is important when it comes to designing major strategies in any enterprise. The large user-generated content requires the use of automated techniques for text mining and analyzing since crowdsourced mining   →

Text Analysis in Excel: Real world use-cases

Recently, we successfully completed beta phase of ParallelDots Excel add-in, a solution for using ParallelDots NLP APIs to do text analysis on unstructured data without writing a single line of code. The Excel add-in is very easy to use and provides a convenient, yet effective solution for your text analysis needs. In an earlier post,   →

Automated Survey Processing using Contextual Semantic Search

With the recent advances in deep learning, the ability of algorithms to analyze text has improved considerably. Now analyzing digital and social media is not restricted to just basic sentiment analysis and count based metrics. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research. We believe it is important   →

Text Classification: Applications and Use Cases

  Text analysis, as a whole, is an emerging field of study. Fields  such as Marketing, Product Management, Academia, and Governance are already leveraging the process of analyzing and extracting information from textual data. In a previous post, we discussed the technology behind Text Classification, one of the essential parts of Text Analysis. Text classification   →

Named Entity Recognition: Applications and Use Cases

Named Entity Recognition is a process where an algorithm takes a string of text (sentence or paragraph) as input and identifies relevant nouns (people, places, and organizations) that are mentioned in that string. In our previous blog, we gave you a glimpse of how our Named Entity Recognition API works under the hood. In this post, we   →

Automated Text Classification Using Machine Learning

Digitization has changed the way we process and analyze information. There is an exponential increase in online availability of information. From web pages to emails, science journals, e-books, learning content, news and social media are all full of textual data. The idea is to create, analyze and report information fast. This is when automated text   →

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