An efficient multiple-word embedding-based cross-domain feature extraction and aspect sentiment classification

Abstract

As the size of the feature space and data size increases, it is difficult to find the essential key features for cross-domain classification problems. Traditional word embedding and feature selection models use limited-sized data and dimensions for feature ranking and classification processes. To overcome these challenges, a hybrid cross-domain classification (CDC) based feature selection has been proposed to improve the efficiency of aspect sentiment classification on large databases. Initially, the crawled data from the Amazon product dataset to the cloud web server. In the data filtering approach, each record is pre-processed for the noise removal process. In the CNN framework, the different word-embedded models are used to ensemble the features from the training data. Here, TF-ID+Word2Vec, Hybrid Glove, and Skipgram are used to select the relevant key features from the training data. On the local side, remote IoT-based devices data distributed processing. For in-depth analysis of the sentiments IOT sensors have been used for automation. The filtered key features are given to the proposed feature extraction measure to select the essential features for the cross-domain classification model, it classifies the positive or negative. The proposed CDC model achieves overall precision and recall is 0.96 and 0.97 respectively. Experimental results proved that the proposed cross-domain feature selection-based classification approach has a better overall true positive than the conventional approaches.