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The benefit of auto-text analysis is that computers can analyze and classify much more data at a much quicker rate than humans and sometimes with better accuracy! Thanks to our NLP team, we now have word clouds that seek out and display trends in negative, positive and neutral words and phrases associated with a specific brand or term.

Advanced word clouds simplify the detection of insights among very high volumes of data. This NLP word cloud displays the positive, negative and neutral terms associated with a new Microsoft TV ad — highlighting areas of concern, and trends in language around the ad. By simply clicking on each word, you can drill down and gain context by viewing the posts associated with each word.

Among many other things, NLP gives you the opportunity to seek out the negative mentions of your brand, to prioritize your engagement and manage your reputation — but also find negative mentions of your competitors, too, for competitive intel and lead generation. For reporting purposes and market research — you can get a high-level global snapshot of the sentiment around your marketing campaigns, and drill down for more detail and context.

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Tutorial: Natural language processing for social media

What is Natural Language Processing? Automated Sentiment Analysis At Synthesio, our technology automatically builds lists of key terms in native languages, so we have the colloquial terms unique to each language. Word Clouds Thanks to our NLP team, we now have word clouds that seek out and display trends in negative, positive and neutral words and phrases associated with a specific brand or term. Julie Meredith T 14 May About the Author: Julie Meredith.

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We use cookies to give you the best user experience on our website. SIMON learns an initial set of data and then applies that knowledge to another domain with significantly reduced training requirements.


  1. What Is NLP and How Does it Work?;
  2. Crimes of the Future: Theory and its Global Reproduction?
  3. Entertainment;
  4. Improving the utility of social media with Natural Language Processing;

SIMON is able to take a tabular dataset and semantically classify it into base classes such as integers, data strings, addresses, etc. Whereas most traditional learning algorithms like Pandas.

Natural Language Processing of Social Media Content

In other words, SIMON is able to take a single input column such as cities and classify the data as a city, a name and as text string. This means SIMON can identify data as multiple labels or classes, such as recognizing data as a mailing address or a geographic location, or both. Whereas the more traditional Pandas. SIMON also outperforms the manual annotations in that it provides a lot more insight into the data, e.

Natural Language Processing for Social Media

But unlike those systems, which leverage a host of metadata beyond the content of the message such as mail server configuration, originating IP address, etc. Check it out in our Github repository here. Want to see it in action? Registration is free and a badge is not required.