基于弹幕数据分析的热门短视频评价研究

Popular Short-video Evaluation Research Based on Barrage Analysis

近年来,web数据挖掘在软件类教学中变得日益重要。该文以哔哩哔哩弹幕视频网为平台,以Python爬虫为工具,搜集大量弹幕数据并做挖掘分析来间接对视频内容进行评价。首先,根据弹幕在视频中出现的时间分布分析视頻最受关注的部分;然后,结合百度AI自然语言处理中的情感倾向分析工具和MATLAB单因素一元方差分析研究视频开头部分的弹幕特征;其次,利用AI语言情感分析和关键词分析方法研究弹幕的情绪和类别,进而分析视频的内容特征。实验最终结果展示了对热门短视頻的独特评价,对于短视频作者和平台有一定的参考价值。以弹幕为基础的研究方法也为视频内容自动化识别及评价提供了一条新思路。

In recent years,web data mining has became increasingly important in software-based teaching.This paper takes the Bilibili screen video network as the platform,takes the Python crawler as a tool to collect a large amount of barrage data,and conduct mining analysis to indirectly evaluate the video content.Firstly,the research shows that the most popular part of videos based on the time distribution of barrages appearing.Secondly,it analyzes the most interesting part of the video based on the time distribution of the barrage in the video.Then,combined with the sentiment orientation analysis tool in Baidu AI natural language processing and one-way analysis of variance in MATLAB,the characteristics of the barrage at the beginning of the video are studied.Thirdly,the AI language sentiment analysis and keyword analysis methods are used to study the emotions and categories of the barrage,and then the content characteristics of the video are analyzed.The final result of the experiment shows a unique evaluation of popular short videos,which has certain reference value for short video authors and platforms.The barrage-based research method also provides a new idea for the automatic identification and evaluation of video content.