Sentiment analysis is the effort to translate human emotion into data that can be used by decision makers to understand their clients. It is, in essence, the data mining of blogs and social networks to examine and summarize reviews, ratings, recommendations and other forms of personal opinion. The tools attempt to categorize statements that are straightforward, such as “I love this product” or “I hate this movie,” as well as those using sarcasm, irony and idioms. Filtering through hundreds of thousands of websites, these algorithms identify trends in opinions and some even identify influential opinion leaders. Such tools could help companies pinpoint the effect of specific issues on customer perceptions, helping them respond with appropriate marketing and public relations strategies. For example, when there was sudden negative blog sentiment against the Yankees, they turned to sentiment analysis to identify the issue. The sentiment analysis identified a problem associated with a rain delayed Yankees-Red Sox game. Stadium officials mistakenly told hundreds of fans that the game had been canceled, but their electronic ticket vendor denied fans’ requests for refunds, on the grounds that the game had actually been played. Once the issue had been identified, the company offered discounts and credits to the affected fans, and re-evaluated its bad weather policy. |