Abstract
Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.
Highlights
Transportation information collection from social networks and its utilization for travel safety are two challenging issues in Intelligent Transportation Systems (ITSs)
To highlight the usefulness of the proposed approach, the performance of the fuzzy ontology + Word2vec was compared with other approaches including ontology with tf-idf, n-gram, and Latent Dirichlet allocation (LDA)
The fuzzy ontology provides semantic knowledge to identify transport features in text and help Word2vec and LSTM to extract those terms that must be used in the task of sentiment classification
Summary
Transportation information collection from social networks and its utilization for travel safety are two challenging issues in Intelligent Transportation Systems (ITSs). Traffic networks can be monitored by sensor devices and social network data. ITSs utilize sensor devices to monitor all aspects of transportation networks. The ITS office uses these devices to gather data about roads and the speed and position of vehicles, etc. After analyzing these data, the ITS office shares public safety notifications regarding traffic or hazards with travelers or notifies the highway department regarding road conditions. ITS may not be able to collect precise traffic information from these sensors
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