Sentiment Analysis of Online Travel Reviews Based on Capsule Network and Sentiment Lexicon

TL;DR

This paper proposes the SCCL model combining capsule networks and sentiment lexicons for improved sentiment analysis of online travel reviews, addressing local feature extraction and emotional semantic understanding.

Contribution

The study introduces a novel SCCL model integrating capsule networks with sentiment lexicons and an improved SO-PMI algorithm for better sentiment classification in travel reviews.

Findings

Enhanced local feature extraction via capsule network

Richer emotional semantic features from sentiment lexicon

Improved sentiment classification accuracy

Abstract

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.