Deep Learning Based Sentiment Classification in Social Network Services Datasets

Abstract

Social network services (SNS) are more and more popular. People are increasingly accustomed to express their opinions on SNS in two ways: (1) product reviews in online shopping sites and (2) posts, comments, tweets, and chats in social network sites. SNS text classification is challenging due to various natural language phenomena, such as spelling mistakes and variations, polysemy, contextual ambiguity and semantic variations. In this paper, we propose a novel deep learning architecture called Hybrid two Convolutional Neural Networks and Bidirectional LSTM (H2CBi) which combines the strength of both Convolutional Neural Networks (CNNs) and Bidirectional LSTM (BLSTM). We use two CNNs for extracting different positive/negative or bullied/no bullied features from SNS data. We use BLSTM to produce a sentence-level representation by maintaining the order of words and get the ability to learn sequential correlations for the sentence which is the negative sentence without having any negative word. We used two kinds of SNS data in this paper: product review data (Amazon, Movie Review and Yelp) and (2) social network sites data (Twitter I, Twitter II, Facebook and FormSpring.me). Some of our H2CBi models, namely 2WH2CBi, WFH2CBi and WGH2CBi have better performance than baseline models in six out of the seven SNS datasets in terms of accuracy and F measure.