Sentiment analysis using neural network

With the rapid growth in use of social networking sites in the past decade, it has become a notable medium for people to express their views or opinions. This has fostered & promoted sentiment analysis as a dynamic & potential area of research where new techniques & models need to be explored for continuous improvement in result accuracy. In this paper, we propose a probabilistic neural network (PNN) with a self-adaptive approach to perform sentiment analysis on tweets. Probabilistic Neural Network as a multi-layered feed-forward neural network is an apt choice because of its prominent features of adaptive learning, fault tolerance, parallelism and generalization which provide a superior performance. Also, the smoothing parameter of PNN plays a great role for predicting an accurate class of classifier. So a self-adaptive algorithm is used to calculate and optimize the smoothing parameter in our research. Two types of Probabilistic Neural Network models are implemented in the proposed approach. First model of PNN, also called as PNNS has single value of smoothing parameter for whole network. Second model, also called as PNNC has different values of smoothing parameter for each class. The training and testing dataset is collected from Twitter using Twitter API. Accuracy of both model PNNS and PNNC is calculated and result shows that the PNNC has a better performance than PNNS.