There are further challenges. For instance, a particular movie review may contain opinions of various polarities – some positive, some negative, and some neutral – and intensities. How do you decide the overall sentiment of the review and similarly understand the aggregate picture, the voice of the market rather than just of individuals? Can you discover relationships between sentiments and the characteristics of the people who expressed them as well trends over time and how opinions propagate through social networks? Can you forecast quantities like box-office receipts from opinions extracted from movie reviews? These analytical steps are the province of traditional data mining and descriptive statistics, which can be (and is being) applied to extracted attitudinal information. The view of Biz360 CTO Mushtaq is that “only a solution that leverages a combination of Information Extraction, Data Mining and Business Intelligence technologies can deliver true actionable intelligence.”
In today's Web 2.0 world, and when working with traditional channels, actionable intelligence may include an understanding of the reach and the influence of opinions. What kinds of view spread fastest and widest? How do they propagate through social networks? Who are the opinion leaders, who are the influencers, and who's listening? These questions can be answered by application of data mining techniques to attitudinal information, completing the sentiment analysis task. 数据挖掘实验室
Jeffrey Catlin, CEO of text-analytics vendor Lexalytics, believes “sentiment analysis has come a long way in the last four years. In certain domains, and under certain uses, it's a very dependable technology.” Nonetheless, accuracy is significantly lower than typically achieved when you stick to named entities and facts and well-structured documents. Text analytics/content management vendor Nstein
