Emotional Analysis Based On Hotel Review Data

With the in-depth development of Internet technology in China and the popularization of modern communication tools,network has become an indispensable part of people's daily life.More and more netizens can fully know the world events without leaving their homes on platforms such as WeChat and weibo.Meanwhile,they can easily and quickly express their own comments and opinions on hot events and public services.In recent years,most of the comments on the data information is from the social network,government departments and related businesses,from these comments in the data we can not only directly experience the emotion of the user,but also find out safety information from the negative reviews data in order to curb accidents,In terms of the hotels and other public services,the accident can be avoided to a great extent if you can seek out the safe hidden trouble early from review data and take protective measures in advance,t.In this paper,according to the requirements of the hotel commodity review analysis system,the overall architecture of the system is designed,the crawler technology is adopted to obtain review data,and the crawled review data is preprocessed like word segmentation.In this paper,Attention mechanism with neural network,the emotional analysis model of hotel review data are proposed.Compared with the method of word2 vec model combined with machine learning,the accuracy rate,precision and recall rate of review classification are all improved correspondingly.The main work of this paper includes:(1)Designed the framework of hotel review data analysis system and implemented it in modules.The system implementation process is described,and the comment data acquisition module,comment data storage module,comment data preprocessing module and result display module are designed and implemented.(2)Realize the dynamic acquisition of hotel review data by combining Python and Selenium,store the crawled data in the MySQL database,and introduce the working principle and data collection process of crawler and anti-crawler in detail in the data crawler module.(3)Based on the comparison between the classical machine learning algorithm and the neural network algorithm,this paper further studies the analysis model of text orientation of hotel review data combined with the Attention mechanism of the neural network,leading to better classification performance.