Sentiment Classification of Reviews on Automobile Websites by Combining Word2Vec and Dependency Parsing

摘要

The online product reviews become one of the most useful and vast information sources for guiding customers' decisions and helping the companies improve the quality of the products and services. Therefore, It is valuable to automatically identify sentiments from comment texts, which is concerned with the Sentiment Classification. In this paper, we propose a novel machine learning-based method called ADSSR to classify the sentiments of reviews on popular automobile websites in China. We extract the features based on dependency parsing which can reveal the syntactic structure of the sentence, to avoid obtaining the same vectors for sentences that contain the same words but a different grammatical structure. In order to reduce the dimensionality of the feature vectors and keep the contributions of the low-frequency words, we obtain the distributed vectors learned by Word2Vec and group the semantic similar words in a cluster through the K-means to obtain the pairs of each word and its corresponding cluster, and then replace every word with its corresponding cluster label. Experiments show the efficiency of the proposed sentiment classification method.