Description
The rapid development and use of Web 2.0 and social media have stimulated user-generated big data. Social and opinion-based websites offer a lot of opportunities for organizations to gather information on their customers’ behavior and needs and sentiment analysis emerged as a technique for analyzing unstructured data. However, there are still problems related to its adoption by businesses. The current study proposes a comparison of two similar methodologies for sentiment analysis which can be utilized by any type of organization. The methods were demonstrated using secondary data from Cars.com, Yelp, and Twitter. The presented methodology provides a detailed guide for practitioners to incorporate sentiment analysis in business operations. It does not require significant investments in infrastructure or specialized software, which makes it much more affordable. Current knowledge on web social intelligence is extended by demonstrating how incorporating a number of methods and tools can be successful for analyzing unstructured text.
Recommended Citation
Bjurstrom, Sean and Plachkinova, Miloslava, "Sentiment Analysis Methodology for Social Web Intelligence" (2015). AMCIS 2015 Proceedings. 9.
https://aisel.aisnet.org/amcis2015/IntelSys/GeneralPresentations/9
Sentiment Analysis Methodology for Social Web Intelligence
The rapid development and use of Web 2.0 and social media have stimulated user-generated big data. Social and opinion-based websites offer a lot of opportunities for organizations to gather information on their customers’ behavior and needs and sentiment analysis emerged as a technique for analyzing unstructured data. However, there are still problems related to its adoption by businesses. The current study proposes a comparison of two similar methodologies for sentiment analysis which can be utilized by any type of organization. The methods were demonstrated using secondary data from Cars.com, Yelp, and Twitter. The presented methodology provides a detailed guide for practitioners to incorporate sentiment analysis in business operations. It does not require significant investments in infrastructure or specialized software, which makes it much more affordable. Current knowledge on web social intelligence is extended by demonstrating how incorporating a number of methods and tools can be successful for analyzing unstructured text.