Text mining using Excel, Semantria, and Tableau

In less than 5 minutes, we take 20,000 tweets from Datasift, perform text mining through the Lexalytics/Semantria Excel Plugin, import the results into Tableau, and start visualizing cool stuff. (This process applies to Tableau version 8.2).

Industry Applications of Text Analytics

An overview of why text analytics is useful, and how it can help areas such as surveys, chat bots, social media monitoring, voice of customer, call-logs, and more.

Why Lexalytics Salience Engine?

Lexalytics provides enterprise text mining (aka text analytics) and sentiment analysis solutions. Salience is an easy to integrate engine that will structure millions of tweets, emails, comments, surveys or any other textual data in a matter of minutes. A text analysis from Salience will extract sentiment bearing phrases, entities and themes from the text. It can also automatically categorize text using a queries - a Boolean login based classifier, or user categories - a classifier based on the semantic knowledge from wikipedia. Head to Lexalytics' website to try a free demo: http://www.lexalytics.com/web-demo

How Text Analytics Helps Reduce Customer Churn Twice as Fast

Dr. Vasudeva Akula of VOZIQ demonstrates how to reduce churn twice as fast, when you add the power of text analytics to a traditional, CRM data based predictive churn model. Please go to http://voziq.com/freetrial/ for their free trial, which lets you analyze up to 1 million agent notes free.

Semantria for Excel - Word Clouds

This tutorial will show you how to generate word clouds from Semantria outputs such as sentiment phrases, themes, and queries.

The Meaning of "Mama"

At Lexalytics we love language. So, to celebrate it, we started this monthly series on how the meaning, etymology and intent of words affect our everyday lives. For our first episode we celebrate the universal beauty of the word "Mama." And, from all of us here at Lexalytics, Happy Mother's Day! Follow us on Twitter @Lexalytics Check out the Demo at semantria.com/demo and don't forget to comment and like :) Thanks for watching! Presented by Chuck Le Gros Filmed and Edited by Michael Drain

Text By the Bay 2015: Seth Redmore, Transforming a Recommender Algorithm into a Multi-lingual Parser

You need solid syntax parsing to really understand the nuance of language. Complicated negation patterns, relationships between entities, entity sentiment assignment (and many other things) are all examples for which having sophisticated syntax understanding is important. The question then is how to get an understanding of syntax across many languages, content types, and contexts. Most traditional model-based approaches require manually coded syntax trees, which are costly to generate, as they require relatively expensive linguist time. These trees exist for some languages, and some content types; but not for, say, German Tweets, or Swedish biotech. It turns out that the problem can be stated as a “similarity” problem, which then looks like a recommendation problem. This presentation will discuss how we leveraged a matrix factorization recommendation algorithm to create a highly efficient, easily extensible syntax parser. Seth Redmore has over 20 years of experience in product marketing and over 10 years of experience in text analytics - from the perspective of a user as well as a vendor. Seth has worked in a number of executive roles at both hardware and software companies, including co-founding Netiverse (who built a high-speed server load balancing system) which was bought by Cisco Systems in 2000. While at Cisco, he worked in a variety of product management and product marketing roles, culminating in building Cisco's internal text analytics solution for reputation management (using Lexalytics' software engine at its core). Seth has been working with Lexalytics, the leading text analytics vendor since 2008. Seth has a degree in Chemistry from Carnegie Mellon University. ---------------------------------------------------------------------------------------------------------------------------------------- Scalæ By the Bay 2016 conference http://scala.bythebay.io -- is held on November 11-13, 2016 at Twitter, San Francisco, to share the best practices in building data pipelines with three tracks: * Functional and Type-safe Programming * Reactive Microservices and Streaming Architectures * Data Pipelines for Machine Learning and AI

Lexalytics User Group - Elizabeth Baran on Salience in Chinese

Computational linguist, Elizabeth Baran, works with Lexalytics as a Chinese language expert. Here she describes why Salience's Chinese language capabilities are the best choice.

Search and Text Analytics, Jeff Catlin for Network World

Jeff discusses how we integrate and work with search engines for Network World.

Lexalytics User Group - Craig Golightly on How to Find Intelligence in VOC and Compliance

Craig Golightly is a senior software consultant with Software Techology Group, based out of Salt Lake City, Utah.

Lexalytics User Group - Seth Grimes: Converging Silos of Text Analysis

Seth Grimes is an analytics strategist with Washington DC based Alta Plana Corporation. He is contributing editor at TechWeb's InformationWeek, founding chair of the Text Analytics Summit, Sentiment Analysis Symposium, and Smart Content conference, and text analytics channel expert for TechTarget's BeyeNETWORK.com. He is the leading industry analyst covering text analytics. Seth consults, writes, and speaks on business intelligence, data management and analysis systems, text mining, visualization, and related topics.

Lexalytics User Group - Tim Mohler's Fun Tricks with Salience

Tim Mohler, VP of Professional Services, takes us through some fun tips and tricks with Salience Engine.

Semantria + Tableau for visual text mining

Using Tableau to visualize text content processed through the Lexalytics Semantria Excel plugin.

Text mining with Microsoft Excel, Semantria and Tableau

In this video, intended for Tableau version 8.1 and earlier, we process 20,000 tweets via the Lexalytics/Semantria Excel plugin, import the results into Tableau, and start running cool visualizations.

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