Design And Implementation Of Sentiment Analysis System For Video Barrage

With the rapid development of the self-media industry,the explosive growth of video on the Internet has brought challenges to the analysis of network public opinion.Traditional video public opinion management mainly relies on user reporting,video sensitive words and sensitive image recognition,etc.,but it is difficult to identify negative and inflammatory videos.Barrage can fully reflect the user’s evaluation and feelings of video content,but its role is often ignored in public opinion analysis and processing.Sentiment analysis on user barrage helps decision makers to quickly discover bad videos,monitor the public’s emotional tendencies towards video-related content,and respond to emergencies.There are internet spoken language,real-time updated internet hot words and emojis in the video bullet chat comment,and the sentiment analysis method using traditional sentiment dictionary and machine learning classification model is not effective for classification.Therefore,there are the following two core problems in video bullet chat sentiment analysis:how to effectively improve the accuracy of sentiment analysis based on the sentiment dictionary,and how to make full use of a large number of unlabeled bullet chat texts to improve the performance of the model.Firstly,this paper proposes a sentiment analysis method based on sentiment dictionary.According to the characteristics of the barrage,the pre-q public subset method and the K-PMI method are proposed to construct the sentiment dictionary in the barrage domain.Filter neutral corpus.Secondly,in view of the problem that there is not a large number of public labeled data sets in the field of barrage sentiment analysis,this paper designs the BarrageMix model,which realizes the prediction of unlabeled data by enhancing the limited labeled data,and uses the TMix linear difference method to fill the discrete data.Transition samples between data distributions enhance the generalization and robustness of sentiment classification models.In addition,this paper conducts sufficient experimental comparison on the constructed video barrage data set.The experimental results show that the method and model proposed in this paper can improve the accuracy of barrage sentiment analysis to a certain extent,which verifies the effectiveness of the scheme.Finally,this paper designs and implements a video barrage sentiment analysis system.The system mainly includes functional modules such as barrage data collection,sentiment dictionary preprocessing and representation,semi-supervised model training,predictive analysis and visual interaction.and video link for multidimensional sentiment analysis.By testing and evaluating the functional modules and performance of the system,it is verified that each module runs correctly,and the system can meet the design requirements and user requirements.