Customer Satisfaction Measurement using Sentiment Analysis

Besides the traditional methods of targeting customers, social media presents its own set of opportunities. While companies look for a simple way with a large number of responses, social media platforms like Twitter can allow them to do just that. For example, by creating a hashtag and prompting followers to tweet their answers to some question they can quickly get a large number of answers about a question while simultaneously engaging their customers. Additionally, consumers share their opinions about services and products in public and with their social circles. This valuable data can be used to support business decisions. However, it is huge amounts of unstructured data that is difficult to extract meaningful information out of them. Social Media Analytics is the field which makes insights out of social media data and analyzes its sentiment rather than just reading and counting text. In this article, we used Twitter data to get insight from public opinion hidden in data. The support vector machine algorithm is used to classify sentiment of tweets whether it is positive or negative and the unigram applied as a feature extraction method. The experiments were conducted using large set of training dataset and the algorithm achieved high accuracy around 87%.