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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (7): 61-72    DOI: 10.11925/infotech.2096-3467.2018.1404
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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)
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Abstract  

[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.
Key words： LDA      Topic Filtering      Topic Similarity Calculation      Topic Semantic Evolution     
Received: 14 December 2018      Published: 06 September 2019
:  TP391 G35  
Corresponding Authors: Yuefen Wang     E-mail: yuefen163@163.com
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Peng Guan
Yuefen Wang
Zhu Fu

Cite this article:

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.

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http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1404     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I7/61

检索策略 内容
检索式 主题: (“lithium-ion battery”)OR主题: (“li-ion battery”)
来源数据库 SCI-EXPANDED, CPCI-S, CCR-EXPANDED, IC
文献类型 ARTICLE OR PROCEEDINGS PAPER
语种 ENGLISH
起止时间 1996-2016
Period1 Total Similarity Period2 Total Similarity Period3 Total Similarity Period4 Total Similarity
TOPIC1-2 TOPIC0 0.6967 TOPIC2-0 TOPIC4 0.6414 TOPIC3-0 TOPIC16 0.5127 TOPIC4-0 TOPIC20 0.7551
TOPIC1-6 TOPIC4 0.6516 TOPIC2-1 TOPIC2 0.6311 TOPIC3-2 TOPIC9 0.6478 TOPIC4-1 TOPIC21 0.7117
TOPIC2-3 TOPIC26 0.5351 TOPIC3-4 TOPIC19 0.5928 TOPIC4-2 TOPIC28 0.5770
TOPIC2-4 TOPIC18 0.6746 TOPIC3-5 TOPIC13 0.7918 TOPIC4-3 TOPIC13 0.8123
TOPIC2-5 TOPIC13 0.7051 TOPIC3-6 TOPIC20 0.6012 TOPIC4-4 TOPIC15 0.7438
TOPIC2-6 TOPIC5 0.5293 TOPIC3-7 TOPIC17 0.6544 TOPIC4-5 TOPIC29 0.8251
TOPIC2-7 TOPIC11 0.5234 TOPIC3-8 TOPIC23 0.7040 TOPIC4-6 TOPIC17 0.8359
TOPIC2-12 TOPIC9 0.5126 TOPIC3-11 TOPIC2 0.5399 TOPIC4-8 TOPIC7 0.6125
TOPIC2-13 TOPIC28 0.5353 TOPIC3-12 TOPIC27 0.6356 TOPIC4-10 TOPIC22 0.6173
TOPIC2-14 TOPIC10 0.7870 TOPIC3-13 TOPIC6 0.6828 TOPIC4-11 TOPIC28 0.7419
TOPIC3-14 TOPIC4 0.7191 TOPIC4-12 TOPIC0 0.6675
TOPIC3-15 TOPIC10 0.8036 TOPIC4-13 TOPIC16 0.5920
TOPIC3-16 TOPIC28 0.5894 TOPIC4-14 TOPIC22 0.7608
TOPIC3-17 TOPIC29 0.7393 TOPIC4-15 TOPIC19 0.6163
TOPIC3-18 TOPIC22 0.7179 TOPIC4-16 TOPIC22 0.8565
TOPIC3-19 TOPIC18 0.8335 TOPIC4-17 TOPIC25 0.7555
TOPIC4-18 TOPIC24 0.7675
TOPIC4-19 TOPIC12 0.8842
TOPIC4-20 TOPIC10 0.8262
TOPIC4-21 TOPIC9 0.8343
TOPIC4-23 TOPIC18 0.7892
TOPIC4-24 TOPIC6 0.7144
Period1 Perid2 Period3 Perid4
# of topic splitting 0 2 2 6
# of topic merging 0 0 1 5
# of new topics 2 7 7 6
[1] 王曰芬, 宋爽, 苗露 . 共现分析在知识服务中的应用研究[J]. 现代图书情报技术, 2006(4):29-34.
[1] ( 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.
[3] 郭红梅, 张智雄 . 基于图挖掘的文本主题识别方法研究综述[J]. 中国图书馆学报, 2015,41(6):97-108.
[3] ( 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.)
[4] 陈必坤, 王曰芬 . 学科结构与演化可视化分析的内容研究[J]. 图书情报工作, 2016,60(21):87-95.
[4] ( 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.
[9] 曾利, 李自力, 谭跃进 . 基于动态LDA的科研文献主题演化分析[J]. 软件, 2014,35(5):102-107.
[9] ( 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.
[11] 刘自强, 王效岳, 白如江 . 多维度视角下学科主题演化可视化分析方法研究——以我国图书情报领域大数据研究为例[J]. 中国图书馆学报, 2016,42(6):67-84.
[11] ( 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.)
[12] 陈伟, 林超然, 李金秋 , 等. 基于LDA-HMM的专利技术主题演化趋势分析——以船用柴油机技术为例[J]. 情报学报, 2018,37(7):732-741.
[12] ( 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.)
[13] 吴菲菲, 陈肖微, 黄鲁成 , 等. 基于语义相似度的技术多主题演化路径识别方法研究[J]. 情报杂志, 2018,37(5):91-96.
[13] ( 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.)
[14] 曲佳彬, 欧石燕 . 基于主题过滤与主题关联的学科主题演化分析[J]. 数据分析与知识发现, 2018,2(1):64-75.
[14] ( Qu Jiabin, Ou Shiyan . Analyzing Topic Evolution with Topic Filtering and Relevance[J]. Data Analysis and Knowledge Discovery, 2018,2(1):64-75.)
[15] 张金柱, 吕品 . 基于主题关联度改进的主题演变和突变分析[J]. 情报理论与实践, 2018,41(3):129-135.
[15] ( 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.
[17] 关鹏, 王曰芬 . 科技情报分析中LDA主题模型最优主题数确定方法研究[J]. 现代图书情报技术, 2016(9):42-50.
[17] ( 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.)
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