Basic understanding of sentiment

  3 m, 20 s

It seems lately that there are more and more companies offering sentiment solutions to a variety of markets. Everything from health care to customer service to financial services and reputation management. But in spite of this, very few prospects seem to really understand what the technology will and won't do for them. Let's start with some basic questions to help you understand more about sentiment:

What does it mean to measure sentiment? How do I know if I really need to use it? That depends entirely on the intentions of the user and the content being measured. If you're looking at customer review data (let's say hotel reviews in this case), then you may be interested in the sentiment of each review for the hotel. Were people happy with their stay at this hotel? This would be an example of document sentiment. It would tell you if the overall review was good or bad, and offer little insight to the details of each review. In this case, processing large amounts of data about the same topic works well. If, however, you're reading a publication like Consumer Reports, then you're probably thinking more about how the different hotels stack up against one another. You'd like to do some comparison. In this case, the overall document sentiment wouldn't be of much help because the document will have some good and some bad content mixed within it. In fact, what the reader really cares about in this kind of content is the tone for each specific hotel that's being described in the document and the reasons why. Were the beds comfy? How was the shower pressure? Is the staff friendly? In some cases the beds may have been comfortable but the staff rude, which can sway the sentiment of a review. Depending on what is important to you, you'd want to extract the sentiment of each entity. This is known as entity-level sentiment.

What really matters in sentiment analysis? Is it the accuracy or the automation? Again, it depends on your needs and goals for using sentiment analysis. An example we often use where a technology-based automated solution really shines is in financial services where the trends across a collection of stories are what users are most interested in. They care less about the accuracy of every document detail, and more about the sentiment across a corpus of data that needs to be processed quickly. Financial Services is definitely one of the up and coming industrial uses of sentiment because the technology tends to perform better than humans in processing large collections of content. Reputation Management is another industry where automated sentiment analysis shines bright, but where accuracy comes under more scrutiny. It could be said that automated sentiment analysis was born in this space, and was invented because of the amount of time people spent hand measuring the tone around products and brands. While Reputation Management is currently the biggest market for the technology, it's probably not the best example of accuracy. It's hard enough to get humans to agree with humans on the tone for a specific story, but to get people to agree with a computer is even harder. I bring up these two contrasting uses because it's important for people to think about their specific needs and requirements before they jump into using any vendor's solution. Make sure the solution you're looking at is well-suited for the problem you're trying to solve. So while there are more claims of sentiment analysis hitting the market, and after 6 years as a company processing unstructured text and watching online content take hold, it's interesting to see how sentiment appears to be somewhat of a commodity. It challenges all the providers to do a better job in all aspects of the technology. However, it's a fact that analysis of good, bad and neutral isn't as easy as 1,2,3. Ask for a proof of concept before making a decision and make sure the solution is right for you and your business.

Categories: Reputation Management, Sentiment Analysis, Social Media, Technology, Text Analytics