Early Detection Method of Service Quality Reduction Based on Linguistic and Time Series Analysis of Twitter

This paper proposes a method for detecting service quality reduction at an early stage based on a linguistic and time series analysis of Twitter. Recently, many people post their opinions about products and service quality via social networking services, such as Twitter. The number of tweets related to service quality increases when service quality reductions such as communication failures and train delays occur. It is crucial for the service operators to recover service quality at an early stage in order to maintain customer satisfaction. Tweets can be considered as an important clue for detecting service quality reduction. In this paper, we propose a method for early detection of service quality reduction by making the best use of the Twitter platform, which includes tweets as text information and has a feature of real time communication. The proposed method consists of a linguistic analysis and time series analysis of tweets. In the linguistic analysis, semi-automatic method is proposed to construct a service specific dictionary, which is used to extract negative tweets related to the services with high accuracy. In the time series analysis, statistical modeling is used for the early and accurate anomaly detection from the time series of the negative tweets. The experimental results show that the extraction accuracy of negative tweets and the detection accuracy of service quality reduction are significantly improved.