Data Analytics
Text mining and sentiment analysis are processes that are typically used concurrently. The former involves deriving information from natural language text whereas the latter does so for the sole purpose of identifying and extracting attitudes about products and companies (Chakraborty). These two practices have improved most in recent years and continue to become more and more popular due to their combined ability to accurately capture tone and opinions towards a product or service from an organic human statement. Essentially, text mining and sentiment analysis allow companies to get a feel for the general public’s view of themselves, their products in real time without relying solely on numbers derived from purchasing patterns, frequency, or generic responses to surveys.
Processes that do analyze strictly numbers and distinct categories are primarily what was discussed in MIS 2502. For example, SAS Enterprise Miner was used in order to take various data sets of user generated data (age, gender) and user actions (purchases, frequency) in order to create profiles of customers through decision trees or clustering or association rules mining. What text mining and sentiment analysis offer to this example is the unique capability of processing the natural language in customer reviews and customer blogs, tweets, or posts on various social media outlets in order to constantly identify their general attitude without waiting for generic surveys to be created, completed, and analyzed.
Kia Motors is just one of the companies that has sought to take advantage of text mining and sentiment analysis. With a new vision for how Kia wants to be regarded by consumers, they’ve chosen this technology for its ability analyze opinions from various web sources in real time. Kia was most interested in determining whether or not the launch of the 2012 Rio would lead the company in its desired direction for consumer perception (King). Market share for texting mining and sentiment analysis will continue to rise as more companies like Best Buy, Cisco Systems, and Intuit, begin to see the potential value in the ability to determine the tone and opinions of customers from natural language text posts, blogs, and reviews.
Chakraborty, Goutam, and Murali Pagolu, Satish Garla. Text Mining and Analysis Practical Methods, Examples, and Case Studies Using SAS®. Cary, North Carolina: SAS Institute Inc., 2013. SAS. Web. 22 April. 2015
King, Rachael. "Sentiment Analysis Gives Companies Insight Into Consumer Opinion." Bloomberg. n.p., 1 March, 2011. Web. 23 April. 2015