Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries
Peng Guan1,2,Yuefen Wang2(),Zhu Fu3
1(School of Economics and Law, Chaohu University, Hefei 238000, China) 2(School of Economics and Management, Nanjing University of Science & Technology, Nanjing 210094, China); 3(School of Economic and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China)
[Objective] This paper tries to identify the trends of topic semantic evolution at different development stages. [Methods] First, we combined the LDA model and life cycle theory to propose an analysis method. It addressed three technical issues, such as filtering topics, calculating topic semantic similarity and identifying topic semantic evolution patterns of lithium ion battery techniques. [Results] We found that topic inheritance ran through the whole process of discipline development. The topic splitting started at the growth stage and achieved 6 at the fast development stage. The topic merging began at the development stage and reached 5 at the fast development stage. [Limitations] More research is needed to determine whether the overall topics can cover all phases of the developments. The knowledge map of topic semantic evolution also needs to be created automatically. [Conclusions] The proposed method could identify key semantic evolution patterns such as inheritance, division and merging in the development stages. It provides valuable decision-making information for the knowledge innovation.
关鹏,王曰芬,傅柱. 基于LDA的主题语义演化分析方法研究 * ——以锂离子电池领域为例[J]. 数据分析与知识发现, 2019, 3(7): 61-72.
Peng Guan,Yuefen Wang,Zhu Fu. Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries. Data Analysis and Knowledge Discovery, 2019, 3(7): 61-72.
( Wang Yuefen, Song Shuang, Miao Lu . Application Study of Co-occurrence Analysis in Knowledge Service[J]. New Technology of Library and Information Service, 2006(4):29-34.)
[2]
Deerwester S, Dumais S T, Furnas G W , et al. Indexing by Latent Semantic Analysis[J]. Journal of the American Society for Information Science, 1990,41(6):391-407.
( Guo Hongmei, Zhang Zhixiong . Methods of Text Theme Identification Based on Graph Mining[J]. Journal of Library Science in China, 2015,41(6):97-108.)
( Chen Bikun, Wang Yuefen . Contents Research of Visualization Analysis of Discipline Structure and Evolution[J]. Library and Information Service, 2016,60(21):87-95.)
[5]
Blei D M, Ng A Y, Jordan M I . Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003,3:993-1022.
[6]
Blei D M, Lafferty J D. Dynamic Topic Models [C]// Proceedings of the 23rd International Conference on Machine Learning. ACM, 2006: 113-120.
[7]
Wang X, McCallum A. Topics Over Time: A Non-Markov Continuous-Time Model of Topical Trends [C]// Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2006: 424-433.
[8]
Blei D M . Probabilistic Topic Models[J]. Communications of the ACM, 2012,55(4):77-84.
( Zeng Li, Li Zili, Tan Yuejin . Analysis of Topic Evolution in Scientific Literature Based on Dynamic Latent Dirichlet Allocation[J]. Software, 2014,35(5):102-107.)
[10]
Hassan S U, Haddawy P . Analyzing Knowledge Flows of Scientific Literature Through Semantic Links: A Case Study in the Field of Energy[J]. Scientometrics, 2015,103(1):33-46.
( Liu Ziqiang, Wang Xiaoyue, Bai Rujiang . Research on Visualization Analysis Method of Discipline Topics Evolution from the Perspective of Multi Dimensions: A Case Study of the Big Data in the Field of Library and Information Science in China[J]. Journal of Library Science in China, 2016,42(6):67-84.)
( Chen Wei, Lin Chaoran, Li Jinqiu , et al. Analysis of the Evolutionary Trend of Technical Topics in Patents Based on LDA and HMM: Taking Marine Diesel Engine Technology as an Example[J]. Journal of the China Society for Scientific and Technical Information, 2018,37(7):732-741.)
( Wu Feifei, Chen Xiaowei, Huang Lucheng , et al. Multi-thematic Evolution of Technology Based on Semantic Similarity[J]. Journal of Intelligence, 2018,37(5):91-96.)
( Zhang Jinzhu, Lv Pin . Topic Evolution and Mutation Analysis Based on Improved Topic Correlation Method[J]. Information Studies: Theory & Application, 2018,41(3):129-135.)
[16]
Palla G, Barabási A L, Vicsek T . Quantifying Social Group Evolution[J]. Nature, 2007,446(7136):664-667.
( Guan Peng, Wang Yuefen . Identifying Optimal Topic Numbers from Sci-Tech Information with LDA Model[J]. New Technology of Library and Information Service, 2016(9):42-50.)
