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What is Sentiment Analysis?

18/11/2019Elli Bourlai, Featured, Text Analytics

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 sentiment polarity in a given text that may express people’s opinions, appraisals, attitudes, and emotions toward entities, individuals, issues, events, topics and their attributes[1]. Put simply, it answers the question “how does the writer feel about this topic?” with the answer positive or negative, but may also include neutral. In other words, Sentiment Analysis identifies words or patterns in language that carry positive, negative, or neutral sentiment. As humans, we interpret sentiment based on not only our knowledge of the…

Improving how mixed sources of data are accurately merged together through the use of fuzzy joins

21/05/2018Chris Farris, Data Mining

In our daily work we often need to combine two or more datasets together into one. This type of operation, known as a join, is rather simple when each record contains a unique ID present in both datasets. However, there are many scenarios where datasets use different methods of creating unique keys and thus do not match or do not have unique keys at all. In these situations the traditional join operation does not suffice. For example, we have many projects involving the analysis of individual people. One dataset may be from one source such as a hospital which will contain medical data for that person while another dataset may be from another source such as an insurance company which will contain policy information. It is unlikely that these two institutions share the same record keeping system in which real world individuals are given the same unique key in both…

Add support for stop list dictionaries when analyzing spelling errors

15/05/2018Jeff Palan, , Text Analytics

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 ACBXYZ) and the occasional single foreign word (e.g. yukata). In order to prevent this dictionary mismatch PolyAnalyst now includes stoplist functionality for spellchecking. In other words you can define a list of words for the spellchecker to ignore without assigning them as actual English words. Without a stop list: With a stop list: no more «yukata»!