Neuro Fuzzy Classification based Traffic Sentiment Research
A. Vijay Karthik1, P. Sengottuvelan2
1A. Vijay Karthik, Muthurangam Government Arts College, Vellore (Tamil Nadu), India.
2P. Sengottuvelan, Department of Computer Science, PG Extension Center, Government Arts College Campus, Dharmapuri (Tamil Nadu), India.
Manuscript received on 11 October 2019 | Revised Manuscript received on 20 October 2019 | Manuscript Published on 02 November 2019 | PP: 430-437 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10650982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1065.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment analysis has become the hot area of research in recent years. Less work is done in the field of transportation. In this paper, Term Frequency – Inverse Document Frequency (TF-IDF) is used for feature selection and Neuro Fuzzy Classification algorithm is designed for Traffic Sentiment Analysis (TSA). It is seen from the results that, the proposed Neuro Fuzzy Rule Mining (NFRM) algorithm yields better performance when compared to the Apriori Algorithm (ARMA) and Fuzzy Rule Mining Algorithm (FRMA) in terms of accuracy, precision, recall and time.
Keywords: Traffic Sentiment Analysis (TSA), Neuro Fuzzy Rule Mining (NFRM), Apriori Algorithm (ARMA), Fuzzy Rule Mining Algorithm (FRMA).
Scope of the Article: Classification