Convolutional Neural Network based sentiment analysis using Adaboost combination

Abstract

Sentimental polarity detection has long been a hot task in natural language processing since its applications range from product feedback analysis to user statement understanding. Recently a lot of machine learning approaches have been proposed in the literature, e.g., SVM, Naive Bayes, recursive neural network, auto-encoders and etc. Among these different models, Convolutional Neural Network (CNN) architecture have also demonstrated profound efficiency in NLP tasks including sentiment classification. In CNN, the width of convolutional filter functions alike number N in N-grams model. Thus, different filter lengths may influence the performance of CNN classifier. In this paper, we want to study the possibility of leveraging the contribution of different filter lengths and grasp their potential in the final polarity of the sentence. We then use Adaboost to combine different classifiers with respective filter sizes. The experimental study on commonly used datasets has shown its potential in identifying the different roles of specific N-grams in a sentence respectively and merging their contribution in a weighted classifier.