Salience Facets: A New Way
Get more out of your text analysis software with Salience Facets from Lexalytics.
These are not "search facets", even though they could be used as search facets - they provide more information than clustering-based search facets.
Salience Facets represent a completely new way to analyze social media and perform text mining.
Salience Five is the first text analytics tool to be able to directly track how real people are describing their real experiences, without pre-configuring a large taxonomy.
Take the sentence “My bed was hard.” No other text analytics product can actually take a collection of hotel reviews and automatically extract “bed” as being an important aspect (or, as we call it, “facet”). Facets have “attributes”, so you can quickly see that there were 10 people who said it was hard, and three people who said it was uncomfortable. Not only do you know that it was negative (from the sentiment analysis), you know why.
Facets are intended to handle cases that aren’t handled well by Themes. Themes present the best combination of intelligence and sentiment scoring for noun phrases, but sometimes you don’t have a good noun phrase to work with, but there’s still meaning and intent to be extracted.
Facets rely on “Subject Verb Object” (SVO) parsing. So, in the case above, “Bed” is the subject, “was” is the verb, and “hard” is the object. In our case, “Bed” is the facet and “hard” is the attribute.
Because of the nature of SVO parsing, we require a collection of content. Any given document is going to have lots of SVO sentences, so, we only bubble the facets or attributes up to the top that occur at least twice.
As such, it should be noted that Salience Facets only work with collections processing in Salience Five. Please see this URL for a discussion of collections processing.
Here’s a specific example, based on a collection of 165 reviews of a cruise liner (there were other facets, but we picked 2 to show you):
Top 5 Attributes for “Ship”
Top 5 Attributes for “Food”
So, yes, it’s a new ship and they’re doing very well with it.
One interesting feature that we’ve added to our Facet processing is the ability to combine Facets based on semantic similarity via our Wikipedia™ based Concept Matrix. We combine attributes based on word stem, and Facets on the semantic distance.
Consider the following example:
You can see how Enterprise and Company are combined into a single facet – which gives richer information by combining the attributes from both.