Sentiment analysis of product reviews in Russian using convolutional neural networks

P. 482–486.

Smetanin S., Komarov M. M.

Nowadays, product reviews on e-commerce sites tend to be a valuable resource in terms of evaluation of customers’ behavior, their preferences, and needs. This paper provides an approach for sentiment analysis of product reviews in Russian using convolutional neural networks. We use Word2Vec pre-trained vectors as inputs for neural networks. This approach utilizes no hand-crafted features or sentiment lexicons. The training dataset was collected from reviews on top-ranked goods from the major e-commerce site in Russia, where the user-ranked scores were used as class labels. The system demonstrated the F-measure score up to 75.45% in a three-class classification. The collected training dataset and word embeddings are available to the research community

Keywords: нейронные сети, natural language processing, neural networks, sentiment analysis, convolutional neural networks, сентиментный анализ, Product Reviews, word embeddings, продуктовые отзывы

In book

2019 IEEE 21st Conference on Business Informatics (CBI)

Vol. 2. , The Institute of Electrical and Electronics Engineers, Inc. , 2019.