Saturday, May 1, 2010

The Intelligence of Social Media (Part 2)

In the first part of this blog, I mentioned that sentiment analysis measures the polarity of opinion—positive, negative, or neutral—regarding a subject, a product, a service, etc.

Two main approaches can be used to perform sentiment analysis or text mining: a knowledge-based approach, which uses linguistic models to classify sentiments; and a learning-based approach, which uses machine learning techniques to classify text. The concept of sentiment analysis opens a great number of possibilities and opportunities for introducing BI strategies to analyze the enormous amount of data flowing through the Web.

In fact, some software solutions have been designed to address this type of analysis. These tools are called “social Web analytics.” According to the definition provided by The Social Web Analytics eBook (2008), by Philip Sheldrake, social Web analytics are “the application of search, indexing, semantic analysis and business intelligence technologies to the task of identifying, tracking, listening to and participating in the distributed conversations about a particular brand, product or issue, with emphasis on quantifying the trend in each conversation’s sentiment and influence.”

Many organizations are aware of the importance of measuring this information and analyzing it. Currently, sentiment analysis has a strong potential to be used jointly with BI applications making it possible to apply traditional BI techniques to visualize what a sentiment-based tool has discovered on the Web. Some vendors are already offering analytics services (radian6, Sysomos, BuzzLogic, and Attentio) to measure and analyze social media content.

Now, there is also an existing trend regarding traditional BI providers to address these tasks:

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