Online reputation measurement of companies based on user-generated content in online social networks

Social media websites such as Facebook, Twitter, etc. has changed the way peoples communicate and make decision. In this regard, various companies are willing to use these media to raise their reputation. In this paper, a reputation management system is proposed which measures the reputation of a given company by using the social media data, particularly tweets of Twitter. Taking into account the name of the company and its' related tweets, it is determined that a given tweet has either negative or positive impact on the company's reputation or product. The proposed method is based on N-gram learning approach, which consists of two steps: train step and test step. In the training step, we consider four profiles i.e. positive, negative, neutral, and irrelevant profiles for each company. Then 80% of the available tweets are used to build the companies' profiles. Each profile contains the terms that have been appeared in the tweets of each company together with the terms' frequencies. Then in the test step, which is performed on the 20% remaining tweets of the dataset, each tweet is compared with all of the built profiles, based on distance criterion to examine how the given tweet affects a company's reputation. Evaluation of the proposed method indicates that this method has a better efficiency and performance in terms of recall and precision compared to the previous methods such as Neural Network and Bayesian method.