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What is sentiment analysis?

Sentiment Analysis is another buzzword that we hear more and more often, but may not entirely understand what it does or how to use it. Sentiment Analysis application in business offers benefits to both companies and customers, such as improving products and services, identifying strengths and weaknesses of the competition, as well as targeted advertising. Sentiment Analysis, also known as Sentiment Detection or Opinion Mining, is the classification of the…
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PolyAnalyst introduces support for using stop list dictionaries when analyzing spelling errors

In previous builds of PolyAnalyst the only way to stop a word from showing up in the spell checker was to add it to the morphology dictionary. This would sometimes result in having to add things to the morphology dictionary that might not really belong there, such as product codes (Model ABC-XYZ) and the occasional single foreign word (e.g. yukata). In order to prevent this dictionary mismatch PolyAnalyst now includes…
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Manual validation of entities in entity extraction

Algorithmic extraction of entities from text is a powerful tool and a core feature of PolyAnalyst, but it can be difficult to get results with absolutely no false positives. Previously, the user would have to reword the algorithm little by little to remove each false positive, or find some other way to filter the results. Fortunately PolyAnalyst supports manual validation of entity extraction results. In effect this means that a…
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Why we use dependency parsing

For beginners, text analysis often begins with a simple keyword search. Analysts can start by searching for “great service”. They may then begin adding other searches like “service was great” or “service is so great”. This, of course, is a very rudimentary approach that will miss a lot of relevant comments, so users who want to get more results quickly will advance to proximity searches. For example, they may search…
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