Guide to Sentiment Analysis Application

January 17, 2014

The article on Lexalytics Blog titled Tagging, Taxonomies, Categorization with Salience provides a guide to using salience to get the most out of data. The first step, Discovery, involves features like Themes which extracts proper noun phrases to give a summary of what the content contains. Step 2 uses Concept Topics which uses ontology built from Wikipedia’s semantic knowledge to relate one word to another.

The article explains how this works:

“Salience will use the relationship between the category samples to tag your data. So every time the word “lion” pops up in your data, that entry will be categorized as “cats”. Every time the word “cheetah” appears, salience will know that this animal belongs to the cat family, and will tag the document as “cats”. This method of categorization is awesome because you do not need to list every single member of the cat family to create this category.”

Step 3 is another way of classifying data; it is creating a query topic. You input all words associated with your topic after consulting Wikipedia and a thesaurus, then limit the search with more information, and you also include how closely one word must be to another for it to be relevant.

Chelsea Kerwin, January 17, 2014

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