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. |
