论文标题
部分可观测时空混沌系统的无模型预测
Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon
论文作者
论文摘要
随着在线旅行服务的开发，它具有良好的应用程序前景，可以及时地挖掘出用户对旅行服务的评估情绪，并将其用作指示改善在线旅行服务质量的指标。在本文中，我们根据社交媒体在线评论研究了在线旅行评论的文本情感分类，并提出了基于胶囊网络和情感词典的SCCL模型。 SCCL模型的目的是缺乏语言模型中文本的局部特征和情感语义特征的考虑，这些特征可以有效地提取文本上下文特征，例如Bert和Gru。然后对其缺点进行以下改善。一方面，基于Bert-Bigru，引入了胶囊网络以提取本地功能，同时保留良好的上下文功能。另一方面，引入了情感词典来提取文本的情感序列，以为模型提供更丰富的情感语义特征。为了增强情感词典的普遍性，基于TF-IDF的改进的SO-PMI算法用于扩展词典，因此词典在在线旅行评论领域也可以很好地表现。
With the development of online travel services, it has great application prospects to timely mine users' evaluation emotions for travel services and use them as indicators to guide the improvement of online travel service quality. In this paper, we study the text sentiment classification of online travel reviews based on social media online comments and propose the SCCL model based on capsule network and sentiment lexicon. SCCL model aims at the lack of consideration of local features and emotional semantic features of the text in the language model that can efficiently extract text context features like BERT and GRU. Then make the following improvements to their shortcomings. On the one hand, based on BERT-BiGRU, the capsule network is introduced to extract local features while retaining good context features. On the other hand, the sentiment lexicon is introduced to extract the emotional sequence of the text to provide richer emotional semantic features for the model. To enhance the universality of the sentiment lexicon, the improved SO-PMI algorithm based on TF-IDF is used to expand the lexicon, so that the lexicon can also perform well in the field of online travel reviews.
