Sentiment is usually categorized into three buckets: positive, negative and neutral. It often get's presented looking something like this:
Sounds pretty simple, right? If content has good words in it, then it's positive. And if the words aren't so nice...well, that can be bad. However, when applying sentiment to any set of content, there is always the chance that what you may think of as good could, in fact, be bad. How about when you automate that process? When we're asked about the "absolute" of automated sentiment, we often use an example from one of our technology customers; they had content that our sentiment engine thought should have be tagged as negative, but was actually positive for them. In their case, they were applying sentiment to product-related content and during the analysis several of the documents included the words "Error Message". In a traditional sentiment situation, anything relating to an "error" would be considered negative, so the engine tagged it as such. After the results were presented, the analysts reviewing the results disagreed with the sentiment engine and concluded that the documents containing "Error Message" were positive. How could that be? Had our automated sentiment gotten it wrong? No. Our software was fine, but since this client believed it to be a good thing that an "Error Message" was thrown when their product failed, they thought of this content as positive. If nothing had been presented in product failure situations, then they would have believed it to be a negative thing. So, something perceived as bad by the software, was in fact good to the client. This is a rare instance, but we use this example to show that sentiment can be subjective, depending on the situation and the content being analyzed. And while automated sentiment helps to expedite the processing time, can be over 80% accurate, and is good in situations where you are weeding out the bulk of neutral content, it is often up to the individual company to dictate how to apply the spectrum of positive - neutral - negative. If you are thinking about applying sentiment to the content you are analyzing, you should know that Lexalytics provides you with both a sentiment score and a confidence score. That is important because it allows you to determine where the good and bad thresholds fall in your world - AND - we let you know how confident we are about our assessment of the good, the bad, and the neutral. When considering sentiment solutions, be wary of the simple red, yellow, green methodology. Without some freedom to move those scales, you may find your analysis will be at the mercy of the technology and you may not always agree with the results.