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.

本文介绍了一种基于社交媒体在线评论的在线旅游评论文本情感分类的 SCCL 模型，该模型利用胶囊网络和情感词库，旨在提高语言模型文本语境特征提取的准确性并增强情感语义信息。