[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以...[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以图书情报领域中2011—2022年Web of Science数据库中的机器学习研究论文为例,融合LDA和Word2vec方法进行主题建模和主题演化分析,引入主题强度、主题影响力、主题关注度与主题新颖性指标识别热点主题与新兴热点主题。[结果/结论]研究结果表明,(1)Word2vec语义处理能力与LDA主题演化能力的结合能够更加准确地识别研究主题,直观展示研究主题的分阶段演化规律;(2)图书情报领域的机器学习研究主题主要分为自然语言处理与文本分析、数据挖掘与分析、信息与知识服务三大类范畴。各类主题之间的关联性较强,且具有主题关联演化特征;(3)设计的主题强度、主题影响力和主题关注度指标及综合指标能够较好地识别出2011—2014年、2015—2018年和2019—2022年3个不同周期阶段的热点主题。展开更多
Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a to...Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.展开更多
文摘[目的/意义]在人工智能技术及应用快速发展与深刻变革背景下,机器学习领域不断出现新的研究主题和方法,深度学习和强化学习技术持续发展。因此,有必要探索不同领域机器学习研究主题演化过程,并识别出热点与新兴主题。[方法/过程]本文以图书情报领域中2011—2022年Web of Science数据库中的机器学习研究论文为例,融合LDA和Word2vec方法进行主题建模和主题演化分析,引入主题强度、主题影响力、主题关注度与主题新颖性指标识别热点主题与新兴热点主题。[结果/结论]研究结果表明,(1)Word2vec语义处理能力与LDA主题演化能力的结合能够更加准确地识别研究主题,直观展示研究主题的分阶段演化规律;(2)图书情报领域的机器学习研究主题主要分为自然语言处理与文本分析、数据挖掘与分析、信息与知识服务三大类范畴。各类主题之间的关联性较强,且具有主题关联演化特征;(3)设计的主题强度、主题影响力和主题关注度指标及综合指标能够较好地识别出2011—2014年、2015—2018年和2019—2022年3个不同周期阶段的热点主题。
基金This work was supported by the National High Technology Research and Development Program of China（No. 2010AA012505, 2011AA010702, 2012AA01A401 and 2012AA01A402）, Chinese National Science Foundation （No. 60933005, 91124002,61303265）, National Technology Support Foundation （No. 2012BAH38B04） and National 242 Foundation （No. 2011A010）
文摘Microblogs have become an important platform for people to publish,transform information and acquire knowledge.This paper focuses on the problem of discovering user interest in microblogs.In this paper,we propose a topic mining model based on Latent Dirichlet Allocation(LDA) named user-topic model.For each user,the interests are divided into two parts by different ways to generate the microblogs:original interest and retweet interest.We represent a Gibbs sampling implementation for inference the parameters of our model,and discover not only user's original interest,but also retweet interest.Then we combine original interest and retweet interest to compute interest words for users.Experiments on a dataset of Sina microblogs demonstrate that our model is able to discover user interest effectively and outperforms existing topic models in this task.And we find that original interest and retweet interest are similar and the topics of interest contain user labels.The interest words discovered by our model reflect user labels,but range is much broader.
