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Traffic congestion is rapidly increasing in urban areas, particularly in megacities. To date, there exist a few sensor network based systems to address thisproblem. However, these techniques are not suitable enough in terms ofmonitoring an entire transportation system and delivering emergency serviceswhen needed. These techniques require real-time data and intelligent ways toquickly determine traffic activity from useful information. In addition, theseexisting systems and websites on city transportation and travel rely on ratingscores for different factors (e.g., safety, low crime rate, cleanliness, etc.).These rating scores are not efficient enough to deliver precise information,whereas reviews or tweets are significant, because they help travelers andtransportation administrators to know about each aspect of the city. However,it is difficult for travelers to read, and for transportation systems toprocess, all reviews and tweets to obtain expressive sentiments regarding theneeds of the city. The optimum solution for this kind of problem is analyzingthe information available on social network platforms and performing sentimentanalysis. On the other hand, crisp ontology-based frameworks cannot extractblurred information from tweets and reviews; therefore, they produce inadequateresults. In this regard, this paper proposes fuzzy ontology-based sentimentanalysis and SWRL rule-based decision-making to monitor transportationactivities and to make a city- feature polarity map for travelers. This systemretrieves reviews and tweets related to city features and transportationactivities. The feature opinions are extracted from these retrieved data, andthen fuzzy ontology is used to determine the transportation and city-featurepolarity. A fuzzy ontology and an intelligent system prototype are developed byusing Prot\'eg\'e OWL and Java, respectively.

Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling