Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie w...Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users’ movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.展开更多
Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the conten...Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the content.The diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual world.This paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion generation.All of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?展开更多
文摘Sentiment analysis is a method to identify and understand the emotion in the text through NLP and text analysis. In the era of information technology, there is often a certain error between the comments on the movie website and the actual score of the movie, and sentiment analysis technology provides a new way to solve this problem. In this paper, Python is used to obtain the movie review data from the Douban platform, and the model is constructed and trained by using naive Bayes and Bi-LSTM. According to the index, a better Bi-LSTM model is selected to classify the emotion of users’ movie reviews, and the classification results are scored according to the classification results, and compared with the real ratings on the website. According to the error of the final comparison results, the feasibility of this technology in the scoring direction of film reviews is being verified. By applying this technology, the phenomenon of film rating distortion in the information age can be prevented and the rights and interests of film and television works can be safeguarded.
文摘Online Social Networks(OSNs)are based on the sharing of different types of information and on various interactions(comments,reactions,and sharing).One of these important actions is the emotional reaction to the content.The diversity of reaction types available on Facebook(namely FB)enables users to express their feelings,and its traceability creates and enriches the users’emotional identity in the virtual world.This paper is based on the analysis of 119875012 FB reactions(Like,Love,Haha,Wow,Sad,Angry,Thankful,and Pride)made at multiple levels(publications,comments,and sub-comments)to study and classify the users’emotional behavior,visualize the distribution of different types of reactions,and analyze the gender impact on emotion generation.All of these can be achieved by addressing these research questions:who reacts the most?Which emotion is the most expressed?
