A Multiclassification Model of Sentiment for E-Commerce Reviews

Abstract

Consumer reviews are important information that reflects the quality of E-commerce goods and services and their existing problems after shopping. Due to the possible differences in consumers' experiences with goods and service quality, consumer reviews can involve multiple-aspect expressions of emotions or opinions. This may result in attitudes expressed by a consumer in the same review sometimes having a variety of emotions. We introduce a sentiment multiclassification method based on a directed weighted model. The model represents the sentiment entity vocabulary as the sentiment nodes and represents the relation between nodes as the directed weighted link. The sentiment entity vocabulary is the entity with attributes, which can express sentiment meaning in related reviews. Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and determined by the direct correlation calculation between them. The paths are all connected directed links from one node to another, which are composed of several nodes and links with close sentiment similarity. Then, we can establish a directed weighted model concerning the sentiments. Directed weighted links having similar sentiment relations with each other may constitute a directed weighted path. There are several directed weighted paths from a start node to the end nodes of the sentiment entity vocabulary in the directed weighted model. Each different path is a different sentiment expression, which represents a different sentiment type. The different sentiment classifications can be obtained through the restriction of path length. Experiments and analysis of the results show that the sentiment multiclassification model based on the directed weighted model proposed in this paper can classify the review sentiments according to different limited threshold rules. Comprehensive analysis indicates the classification results have good accuracy and high efficiency.