Text Analysis

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,   →

Announcing Google Sheets add-on for ParallelDots APIs

We are getting approvals from Google to authorize our add-on. Meanwhile, please use our Excel Plugin. Apologies for any inconvenience. We will keep you updated as soon as Google approves our add-on. Last December, we announced ParallelDots Excel Add-in which provides state-of-the-art text analysis capabilities without writing a single line of code. As our primary   →

Announcing the Launch of Our New Text Analytics Excel Add-In version: Here’s what improved!

Last, November we launched Excel Add-In as an important part of our offering for users who want to use our text analytics products without writing a single line of code.The Add-in since has been downloaded more than 1,000 times and used to analyze more than million lines of text. We also got several feedbacks from   →

Applications of Sentiment Analysis in Business

Sentiment Analysis in business, also known as opinion mining is a process of identifying and cataloging a piece of text according to the tone conveyed by it. This text can be tweets, comments, feedback, and even random rants with positive, negative and neutral sentiments associated with them. Every business needs to implement automated sentiment analysis.   →

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   →

Nudity Detection and Abusive Content Classifiers – Research and Use cases

Web 2.0 revolution has led to the explosion of content generated every day on the internet.  Social sharing platforms such as Facebook, Twitter, Instagram etc. have seen astonishing growth in their daily active users but have been at their split ends when it comes to monitoring the content generated by their users. Users are uploading   →

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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