An Adapative Neuro Fuzzy Inference Sentiment Analysis using Nonlinear SVM

  • Katta Padmaja, Nagaratna P.Hegde


In this paper we compute the sentiment of social media posts using a set of fuzzy rules involving two lexicons and datasets. The proposed Hybrid adaptive fuzzy system integrates NLP ,word sense disambuigation and SVM-Nonlinear. Using four unsupervised fuzzy rule based system to classify the tweet posts into positive, negative sentiment. we used SVM-Nonlinear to optimize the rule set generated for two classes by ANFIS approach. We perform a comparative analysis of our method on  two public twitter dataset, two sentiment lexicons and three approaches (ANFIS,ANFIS-Linear SVM,ANFIS-Nonlinear SVM)for unsupervised sentiment analysis. The fusion of Hybrid Adaptive neuro fuzzy  system with SVM-Nonlinear for sentiment classification provides a new paradigm in sentiment analysis. ANFIS-Nonlinear outshines ANFIS and ANFIS-linear in classification of individual tweets to positive ,negative. this classification helps in producing predictions of political assessment with 90% accuracy.

Keywords: Social media, Twitter, Support vector machine, Adaptive neuro-fuzzy inference system, Political analysis.

How to Cite
Katta Padmaja, Nagaratna P.Hegde. (2020). An Adapative Neuro Fuzzy Inference Sentiment Analysis using Nonlinear SVM. International Journal of Advanced Science and Technology, 29(05), 2506 - 2523. Retrieved from