Sentiment Analysis of Online New Energy Vehicle Reviews

Abstract

Massive online reviews of new energy vehicles in China are deemed crucial by companies, as they offer valuable insights into user demands and perceptions. An effective analysis enables companies to swiftly adapt and enhance their products while upholding a positive public image. Nonetheless, the sentiment analysis of online car reviews can pose challenges due to factors such as incomplete context, abrupt information bursts, and specialized vocabulary. In this paper, an enhanced hybrid model is introduced, combining Enhanced Representation through kNowledge IntEgration (ERNIE) and a deep (Convolutional Neural Network) CNN, to tackle these challenges. The model utilizes fine-tuned ERNIE for feature extraction from preprocessed review datasets, generating word vectors that encompass comprehensive semantic information. The deep CNN component captures local features from the text, thereby capturing semantic nuances at multiple levels. To address sudden shifts in public sentiment, a channel attention mechanism is employed to amplify the significance of crucial information within the reviews, facilitating comment relationship classification and sentiment prediction. The experimental results demonstrate the efficacy of the proposed model, achieving an impressive accuracy rate of 97.39% on the test set and significantly outperforming other models.