Social Media Sentiment Analysis with Lexalytics

I have been working on Speech Recognition and Natural Language Understanding technologies for over 12 years.  Hence, it is natural for me to look at things from this perspective.  When researching social media tools I came across a company called Lexalytics.  Although they may not be a sexy report generating company they have some real horsepower when it comes to NLP.

No public signup available but there is a web demo at the following location: http://www.lexalytics.com/web-demo.  The demo takes text and performs sentiment analysis.  The idea is to get a measure of the authors overall attitude in their post.  Additionally it is often useful to understand attitudes towards specific entities in the text.  There are all kinds of applications such as understanding how your customer base feels about a new product release or social campaign to better understanding your friends reaction to a news story.  You can read more on Wikipedia if you are interested: http://en.wikipedia.org/wiki/Sentiment_analysis.

In order to run the demo I needed some content so I went to the Entre 528 blog page and took the most recent post.  Wix: Quickly build your personal brand – thanks Mohamad for the data.  I pasted the data in and hit process text.  It appears that the overall sentiment is positive with a score of .354.  I believe this indicates that the post is moderately positive overall.

lexalytics1

Below is a breakdown of specific entities, themes and topics that were present in the post.  This section is more interesting because it allows us to see Mohamad’s sentiment towards specific things as well as overarching themes.

lexalyticsreport

The benefit from a single document is minimal but imagine that this could be applied to all relevant data such as blogs, tweets or Facebook posts.  You could instantly know how your customers feel simply by extracting it from their discussions with others.

Natural language processing has been around for decades but recently is gaining significant momentum because of massive amounts of available data and the processing power to deal with it.  The NLP community has benefited greatly from the BigData revolution.  There is no question that text processing approaches will play a vital role in social media analytics moving forward.  There is just too much data for individuals and companies to deal with and we will need to rely on text processing and machine learning algorithms to do it for us.  I’ll be on the look out for any other tools and will post if I find anything worthwhile.

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