Abstract
More and more tourists are sharing their travel feelings and posting their real experiences on the Internet, generating tourism big data. Online travel reviews can fully reflect tourists’ emotions, and mining and analyzing them can provide insight into the value of them. In order to analyze the potential value of online travel reviews by using big data technology and machine learning technology, this paper proposes an improved support vector machine (SVM) algorithm based on travel consumer sentiment analysis and builds an Hadoop Distributed File System (HDFS) system based on Map-Reduce model. Firstly, Internet travel reviews are pre-processed for sentiment analysis of the review text. Secondly, an improved SVM algorithm is proposed based on the main features of linear classification and kernel functions, so as to improve the accuracy of sentiment word classification. Then, HDFS data nodes are deployed on the basis of Hadoop platform with the actual tourism application context. And based on the Map-Reduce programming model, the map function and reduce function are designed and implemented, which greatly improves the possibility of parallel processing and reduces the time consumption at the same time. Finally, an improved SVM algorithm is implemented under the built Hadoop platform. The test results show that online travel reviews can be an important data source for travel big data recommendation, and the proposed method can quickly and accurately achieve travel sentiment classification.
Highlights
In the era of big data, the whole tourism industry is undergoing a revolution
This paper proposes an support vector machine (SVM) classification solution based on the concurrent processing technology of massive data, and designs and implements a classifier based on the improved SVM algorithm in the context of big data
(2) The key technology for analysis of sentiment vocabulary is classification; this paper proposes to classify sentiment of travel online review data by machine learning algorithm and designs an improved SVM algorithm
Summary
In the era of big data, the whole tourism industry is undergoing a revolution. As the scale of tourism development continues to grow, tourism data information is exploding. The main work of this paper is reflected in the following two points: (1) This paper obtains standardized text data by word separation of travel reviews, so as to exploit the value of travel online reviews by using sentiment analysis technology, and makes an innovative exploration in the application. (2) The key technology for analysis of sentiment vocabulary is classification; this paper proposes to classify sentiment of travel online review data by machine learning algorithm and designs an improved SVM algorithm. The rest of the paper is organized as follows: In section 2, the text pre-processing of online travel comment data is studied in detail, while section 3 provides the detailed classification model of tourism emotion based on machine learning. Error Tolerance Since the combination of Chinese vocabulary is very different and the computer has limited ability to recognize Chinese text vocabulary, there will be various errors misleading the final
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