Text Sentiment Analysis for Intelligent Transportation Systems by Using Web-Based Traffic Data: A Descriptive Study

Transportation systems serve the people in essence, but the modern intelligent transportation systems (ITSs) failed to be concerned about public opinions. For the completeness of ITS space, it is necessary to collect and analyze the public wisdom and opinions. However, only a few studies focused on the field of transportation, which failed to meet the stringent requirements of safety, efficiency, and information exchange of intelligent transportation systems (ITSs). We propose traffic sentiment analysis (TSA) as a new tool to tackle this problem, which provides a new perspective for modern ITSs. Methods and models in TSA are proposed in this work, and the advantages and disadvantages of rule- and learning-based approaches are analyzed based on web data. Practically, we applied the rule-based approach to deal with real problems, presented an architectural design, constructed related bases, demonstrated the process, and discussed the online data collection. Two cases were studied to demonstrate the efficiency of our method: the “yellow light rule” and “fuel price” in China. Our work will help the development of TSA and its applications. This research work proposing a novel classification system for our future enhancement. To overcome this issue, we use a fuzzy extension known as the Fuzzy Naive Bayes classifier for the opinion mining process that generalizes the meaning of an attribute so it does not have exactly one value, but a set of values to a certain degree of truth. This fuzzy naive Bayesian classification is consisting of a hybrid classifier bringing together Fuzzy Set Theory and a Naive Bayes classifier.