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

In essence, transportation systems serve people, yet modern intelligent transportation systems (ITSs) have failed to consider public opinion. It is required to collect and analyse public wisdom and opinions in order to complete the ITS space. However, only a few research concentrated on the subject of transportation, which failed to meet the severe standards of intelligent transportation systems in terms of safety, efficiency, and information sharing (ITSs). We suggest traffic sentiment analysis (TSA) as a novel approach for addressing this issue, which gives modern ITSs a fresh perspective. This paper proposes TSA methods and models, as well as a comparison of the benefits and drawbacks of rule- and learning-based approaches using online data. We used the rule-based approach to solve real-world challenges, presented an architectural design, built associated bases, illustrated the process, and talked about online data collecting. The “yellow light rule” and China’s “fuel price” were used to demonstrate the efficacy of our strategy. Our efforts will contribute to the advancement of TSA and its applications. This research paper proposes a new classification system for future development. To solve this problem, we employ a fuzzy extension known as the Fuzzy Naive Bayes classifier for the opinion mining process, which generalises the meaning of an attribute so that it has a collection of values to a specific degree of truth rather than a single value. This fuzzy naive Bayesian classification uses a hybrid classifier that combines Fuzzy Set Theory with a Naive Bayes classification.

Author(s) Details:

R. Jayavadivel,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka – 560064, India.

N. Rajkumar,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka – 560064, India.

B. Prabhu Shankar,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka – 560064, India.

E. Vetrimani,
Department of Computer Science and Engineering, School of Engineering, Presidency University, Bangalore, Karnataka – 560064, India.

C. Viji,
Department of Computer Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka – 560045, India.

G. Aarthy,
Department of Computer Science and Engineering, HKBK College of Engineering, Bangalore, Karnataka – 560045, India.

Please see the link here: https://stm.bookpi.org/RAMRCS-V10/article/view/6270

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