Design And Implementation Of Sentiment Classification System For Tweets In Social Network

With the development of Web 2.0 technology and the rapid popularization of user-generated content based social network application such as Facebook,Twitter and Sina Weibo,more and more people are willing to share their feelings and views on the Internet rather than in real life.How to conduct emotional analysis of these user generated content is a problem which is worth studying.User-generated content is mainly text and image,but the current research on visual sentiment analysis is lagging compared with text sentiment analysis.This is caused by the difficulty and subjectivity of visual sentiment analysis itself.Compared with the more mature text sentiment classification algorithm,the research of this project focuses mostly on improving the effect of visual sentiment classification algorithm and joint visual and textual sentiment classification algorithm.At present,visual sentiment classification problem faces two problems.Firstly,the current visual sentiment classification algorithm originates from the image classification problem.Without improving according to the characteristics of the visual sentiment classification task itself,the effect of the algorithm is not ideal.Secondly,lack of large dataset is one of the bottlenecks that restrict the effect of algorithm.This is due to the strong subjectivity of image emotion classification task,which will bring difficulties to the selection of data annotators and the annotation process.In the joint visual and textual sentiment classification problem,it is the most common method to extract visual feature vectors and textual feature vectors separately and combine them as the input feature of the classifier.At present,the problem is how to introduce the correlation between the two contents in the process of model learning.Based on the above analysis,the main work of this subj ect is as follows:1)An image sentiment classification algorithm is proposed.The algorithm adds the ability of extracting emotional features from multi-level images to the convolutional neural network part,and explicitly introduces the feature interactions of multi-level emotional features in the classifier part.Experiments on open datasets verify the effectiveness of the classification algorithm.2)A training method of visual sentiment classification model is proposed.This project designs and implements an automatic annotation method for sentiment categoriy of Internet pictures.Automatic annotation data and manual annotation data are used in the process of model training at the same time.Experiments on open datasets demonstrate the effectiveness of the proposed method.3)A prototype system of sentiment classification for tweets in social network is designed and implemented.This project designs a prototype system of sentiment classification for tweets in social network.This prototype system can provide sentiment information of social network tweets and can be used for model training by collecting users' feedback.This project has carried on the demand analysis to this system.According to the system requirement,the whole scheme of the system is designed,and each function module is designed in detail.The realization and test of the system function module are completed according to the detailed design.