# Research on Sentiment Classification of Online Travel Review Text
## 1 Introduction
[13] compared automatic text sentiment analysis classifiers with humans and evaluated whether various types of automatic classifiers are suitable for typical applications in the tourism, hotel, and marketing research contexts. The article argues that on difficult and noisy datasets, automatic classifiers achieve worse performance than humans. It can therefore be concluded that the existing sentiment analysis technology needs to be improved to enable the analysis of specific data. Contemporary researchers have proposed many effective solutions to improve the performance of SVM in sentiment analysis. Successful feature extraction is one of the main challenges faced by machine learning methods [24]. Feature extraction can reduce information loss and achieve improved discrimination ability in sentiment classification [25] tasks. In their study of feature selection methods, [PERSON] et al. [25] proposed a Gini index feature selection method based on SVM to carry out sentiment classification for a large movie review dataset. [PERSON] et al. [26] proposed a robust classification technology based on SVM and fuzzy domain ontology (FDO), used for the recognition of comment features and the mining of semantic knowledge. Their experimental results showed that the integration of FDO and SVM greatly improves the accuracy of extracting comments and opinion words, as well as the accuracy of opinion mining. [PERSON] et al. [27] proposed a new feature selection method based on the query expansion term weighting method in the information retrieval context. This study uses four classifiers to compare their method with other widely used feature selection methods, thereby verifying their method's effectiveness. [PERSON] et al. [28] proposed a latent semantic analysis (LSA) and random projection (RP) feature selection method for the sentiment analysis of Twitter data, and thereby constructed a new Twitter mixed sentiment classification method. [PERSON] et al. [29] introduced swarm intelligence algorithms into the field of feature optimization in order to improve the sentiment classification performance accuracy. [PERSON] et al. [30] used a variety of features to identify candidate opinion sentences, then used structured SVM to encode these opinion sentences for document sentiment classification. This article resolves the issue of sentiment classification problems arising when the sentiment of most sentences is inconsistent with the sentiment of the document overall. As an effective feature selection method, semantic expansion has also been widely studied. [PERSON] et al. [31] designed a sentiment analysis model based on a naive Bayes classifier and the semantic extension method, proving that the semantic extension method can improve the accuracy of sentiment analysis. [PERSON] et al. [32] integrated the context features extracted from the comment sentences and the external knowledge retrieved from the sentiment knowledge graph into a neural network to compensate for the lack of available training data, consequently obtaining better sentiment analysis results. At the same time, as an effective channel for semantic expansion, knowledge bases such as WordNet and ConceptNet are widely used in sentiment analysis in multiple languages. [PERSON] et al. [33] proposed using Arabic WordNet as an external knowledge base to enrich the representation of tweets due to the weakness of the bag of words model; the use of naive Bayes and SVM on the Arab Twitter dataset verified that this external knowledge base can be used to improve sentiment analysis accuracy. [PERSON] et al. [34] used Persian WordNet to generate a review corpus, proving that sentiment dictionary quality plays a key role in improving the quality of sentiment classification in the Persian language. Moreover, [PERSON] et al. [35] proposed a novel sentiment analysis model based on ConceptNet and common sense extracted from context information. At the same time, a number of scholars in tourism research have studied the application of sentiment analysis to tourism and hospitality-related data.