Analysis of opinion leaders using text mining techniques on social media

The advent of social media technology has witnessed rapid adoption globally. There are many ways in which people engage in using this technology. Some people use this technology for fun, some for commercial purpose, while others use it for education. The social media has become essential tool in the lives of people. Nowadays, people can check what other customers think about a product or service before they buy on the Internet. Opinion can be formed referring to shared feedback on the virtual community which redirect the new opinion of people. There are few individuals who can influence buying decisions, ideas or even political ambitions of others through the comments they made on the social media. Those individuals are usually referred to as opinion leaders or influential leaders. It is quite possible to reach out and communicate with a large number of people on the social media through the opinion leaders. The importance of these opinion leaders in marketing made companies start to change their marketing strategies. However, identifying such opinion leaders is not as easy as it may seem. So, the identification and analysis of opinion leaders on social media is a valuable research. This study identifies and analyzes the opinion leaders on Twitter social media site. The first method used to identify the opinion leaders is the traditional measure of influence called indegree, the second one is the retweets criterion, and finally a measure of influence based on sentiment analysis is proposed. The sentiment analysis method uses Naïve Bayes classifier for the classification of sentiments polarity and it has an accuracy of 59.51%. The analysis based on retweets and sentiments showed that having a high indegree rank (the traditional ranking score) does not necessarily mean a user is an opinion leader. Keywords: Influential Leaders, Naïve Bayes, Opinion Leaders, Opinion Mining, Text Mining