KL-NF technique for sentiment classification

This work proposes sentiment analysis for low-resource languages like Hindi using Neuro-Fuzzy Technique. Low-resource languages suffer from the scarcity of resources; consequently, we propose a method that can be implemented for any language. We use information theory for establishing a relation between terms that exists in a sentence. This work proposes a novel approach for calculating feature values using Kullback-Leibler (KL) divergence method. The feature values are employed to calculate the membership values associated with the Fuzzy logic in Neuro-Fuzzy Technique. The novelty of this method lies in its predictive nature that can mitigate the impact generated from un-labeled, unknown data or multi-domain data. We have seen the results for multi-domain data in our experiments. We evaluate our results using Accuracy, Precision, Recall and F1-Score. Our experiments show the efficacy of the proposed approach. It achieved 93.01% accuracy for English dataset and 91.18% accuracy for Hindi dataset which is more than the other state-of-art techniques like Naïve Bayes and SVM. Additionally, we found that our approach provides satisfactory results with multi-domain data as both the datasets were of different domains.