Deep Learning and Ensemble Approach for Praise or Complaint Classification
Sujata Khedkar, Subhash K. Shinde · Procedia Computer Science · 2020
Online reviews, social media data contains wealth of information about customer’s likes and dislikes about product or service. Extreme opinions in terms of praises and complaints are more informative as compared to plain positive and plain negative sentences and are difficult to find from customer reviews. Existing approaches to sentiment analysis classify the sentences as positive negative or neutral and do not consider informativeness of sentences. This paper compares Machine Learning, Ensemble and Deep Learning based approaches for praise or complaint classification using hybrid features based on linguistic properties of extreme opinions. The performance of the Machine Learning classifiers with proposed hybrid features and Deep Learning based classifiers using Dense Neural Network, CNN, Multichannel CNN were evaluated by training and testing the deep neural network with a set of important words such as nouns, adjectives, intensifiers, verbs, etc present in the sentence. The proposed Ensemble classifier uses SVC, Random Forest, Decision Tree, MLP classifiers. Hotel domain reviews were evaluated using the parameters Accuracy, Precision, Recall, and F1-score. The evaluation results show that the proposed Ensemble classifier and Deep Learning Models provides better classification accuracy as compared to existing methods.