Carl reads between the lines of the final presidential debate of 2016. In this analysis, draws together conclusions unearthed by Salience across this and the previous three presidential and vice presidential debates.
We continue following the candidates through the rabbit hole of the debates. Processing key statements through our Salience text analytics engine allows for a new perspective on what is being said in the political arena.
Applying data science to politics is one way we can hold our elected representatives accountable. As the cacophony of the first Presidential debate settles, we turn our Salience engine onto each statement made by the candidates and see what was really said.
Here at Lexalytics, we’re excited to be in beta with Salience Text Analysis for Chinese. There are many features in our toolkit – sentiment, topic detection, summarization, theme extraction – but sentiment is what we’ve […]
At Lexalytics, we know it’s not only a global marketplace, but a multi-lingual global marketplace. It’s this understanding which has driven us to extend the capabilities of Salience beyond analysis of English. Salience was originally […]
I recently attended Text Analytics World, an annual text analytics conference that took place in Boston two weeks ago. The two main themes were “big data” and “social media”. There was some discussion around sentiment […]
I’ve seen many postings advising companies on listening to their customers, especially as new voice of the customer outlets such as Twitter (can we really still call Twitter “new”) evolve and grow in usage and […]
The quick answer is “No, you don’t.” But as you would expect, it’s more complicated than that. You do own your online reputation, to the degree that you are actively tracking and managing it. There […]