Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Abstract

Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores 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 and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.

Opinion Mining on Social Media Transit Tweets using Text Pre-Processing and Machine Learning Techniques

“…[2] Proposed a deep learning approach for detecting accidents using social media data. [3] Presented a fuzzy ontology-based opinion mining of transportation city features such as bus and train stations, parks, restaurants, etc., for safe and secure traveling and for increasing the transportation facilities. [4] discussed a methodology that scrutinizes and visualizes the geotagged social media data to know the destination choice and business clusters.…”

“…tf idf = tf ij * idf i (3) Where tf ij is the term frequency of term i and idf i is the inverse document frequency of term i. [41], [42], [46].…”

“…The experiment is performed on all combinations. Performance metrics such as accuracy, precision, recall, and f-measure is presented from Fig.1 to Fig.14. The confusion matrix is shown in Table I [3], where TP, TN, FP, and FN represent true positive, true negative, false positive and false negative in the information retrieval. Precision is defined as a number of retrieved elements that are relevant.…”

Value Predicate Analysis: A Language‐Based Tool for Diagnosing Behavioral Tendencies of Religious or Value‐Based Groups in Regions of Conflict

“…2 Well-known examples of qualitative semantic analysis include what researchers call critical discourse analysis (Blommaert and Bulcaen 2000;Weiss and Wodak 2003;Woodak 1997): 3 well-known examples of quantitative analyses include sentiment analysis, a binary measure of the negative or positive valences of the meanings of value-related (or evaluative) words, which data scientists correlate with correspondingly negative or positive future behaviors. 4 Data scientists have successfully applied sentiment analysis to the challenges, for example, of predicting near future traffic accidents (Farman Ali et al 2017;Jingrui He et al 2013;Zhang et al 2018), by the hour, at certain city intersections and of predicting near-future violent crime, by the week or day, in certain city streets (Delavallade, Bertrand, and Thouvenot 2017;Wang, Gerber, and Brown 2012). Globally, many expensive, large-scale projects have been funded to apply sentiment analysis to the prediction of terrorist activity among various groups, including groups classified as religious.…”

“…Data scientists have successfully applied sentiment analysis to the challenges, for example, of predicting near future traffic accidents (Farman Ali et al. ; Jingrui He et al. ; Zhang et al.…”