Lexalytics Sentiment Spectrum

  1 m, 47 s

Sentiment Analysis solutions are popping up everywhere these days – or so it seems. Every day there is a blog post, or Twitter post (or 100), asking how it works or arguing a point about sentiment and what exactly it means. There has been an increase in articles covering everything from automated solutions vs. human analysis, to accuracy, to processing online content along with traditional content, to analyzing customer conversations. So, as a text analytics provider that has been offering sentiment analysis for years now, we thought it was about time we introduce a guide that organizations could use when they’re trying to decide what they need for an analysis solution. Lexalytics is pleased to share the Lexalytics Sentiment Spectrum. It is a view of all the factors that may come into play when deciding which route is best for your company.


Our hope is that by looking at the various factors that go into extracting sentiment analysis, along with the different methods by which it can be implemented, it will become a little less confusing on which process may be best for your organization. The key questions we believe you need to ask include:

  • Is my data public or private? What type of security do I need on it?
  • Will I require any customization of dictionaries or integration to an existing application?
  • What does it cost?
  • What are my accuracy expectations?
  • Are we processing 100 documents a day or 100 documents a minute?
  • How many sources do I have flowing into my system?
  • Do I want to process online content? In house content? Both?
  • Does the solution give me sentiment of a whole document, or all the things contained within the document?

As we always do, we suggest you talk to a variety of vendors to review these key points and to ask for a possible proof of concept. Sentiment is inherently different for each company depending on what it is they need to analyze and accomplish – and how much human interactions is going to be involved. Some industries can use automated sentiment with little interaction at all and others need additional validation or customization to get the perfect results.

Categories: Sentiment Analysis