Subject Topic Mining and Evolution Analysis with Multi-Source Data

doi:10.11925/infotech.2096-3467.2021.1296

Data Analysis and Knowledge Discovery

2022, Vol. 6

Issue (7): 44-55 DOI: 10.11925/infotech.2096-3467.2021.1296

Original article

Current Issue | Archive | Adv Search

Subject Topic Mining and Evolution Analysis with Multi-Source Data

Li Hui(

),Hu Jixia,Tong Zhiying

School of Economics and Management, Xidian University, Xi’an 710126, China

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Abstract

[Objective] This paper examines the evolution of research topics, which helps researchers quickly identify the status quo and trends in their fields. [Methods] First, we merged multi-source datasets and divided the domain research topics by time period. Then, we calculated topic importance with their popularity, density, and closeness centrality. Third, we utilized topic semantic similarity to identify the related ones from adjacent time periods. Finally, we combined the topic importance fluctuation and the topic similarity to decide their evolution types and paths. [Results] We examined our model with papers on artificial intelligence and analyzed the changes of topics in the past 20 years. We identified the popular research topics and their evolution paths, which showed obvious thematic fusion and split development in four periods. [Limitations] The topic naming rules could be more effective and we could not show the whole life cycle of the booming artificial intelligence research. [Conclusions] The proposed model could effectively reveal the topic evolution of research.

Key words： Topic Evolution LDA Topic Similarity Evolutionary Type Multi-Source Data

Received: 13 November 2021 Published: 24 August 2022

ZTFLH:

G254

Fund:National Natural Science Foundation of China(71203173)

Corresponding Authors: Li Hui,ORCID:0000-0002-3468-5170 E-mail: lihui@xidian.edu.cn

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Cite this article:

Li Hui, Hu Jixia, Tong Zhiying. Subject Topic Mining and Evolution Analysis with Multi-Source Data. Data Analysis and Knowledge Discovery, 2022, 6(7): 44-55.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1296 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I7/44

Flow Chart of Subject Evolution Path Analysis

Pointer Generation Network Model

演化类型	判定条件	描述
生长	$s i m (T i t - 1, T j t) ≥ ρ$ 且 $f l u (T i t - 1, T j t) > λ$	当前主题研究热度上升
延续	$s i m (T i t - 1, T j t) ≥ ρ$ 且 $- λ ≤ f l u (T i t - 1, T j t) ≤ λ$	当前主题研究热度基本不变
衰减	$s i m (T i t - 1, T j t) ≥ ρ$ 且 $f l u （ T i t - 1, T j t ） < - λ$	当前主题研究热度下降
新生	前一时间段内所有主题 $T t - 1$ , $s i m (T t - 1, T j t) < ρ$	当前主题在前一时间段中不存在
融合	$s i m (T i t - 1, T j t) ≥ ρ$ 且 $s i m (T k t - 1, T j t) ≥ ρ$	当前主题是由前一时间段的多个主题合并产生的
分化	$s i m (T i t - 1, T j t) ≥ ρ$ 且 $s i m (T i t - 1, T k t) ≥ ρ$	前一时间段的某一个主题在当前时间段中分裂成多个
消亡	当前时间段所有主题 $T t$ , $s i m T i t - 1, T t < ρ$	前一时间段的某个主题在当前时间段中消失

Criteria for Determining the Evolution Type of Topic Content

Data Source

模型参数	参数说明
$α$	文本集在潜在主题上的狄利克雷先验,α=50/K
$β$	潜在主题在特征词集上的狄利克雷先验,β=0.02
K	最优主题数60
niters	Gibbs抽样迭代次数,niters=1 000
twords	主题下特征词个数,twords=30

Description of LDA Model Parameters

TSNE Dimension Reduction Visualization for “Year-Topic” Matrix

Time Segmentation Result

Perplexity Curve for Each Time Period

时间段	主题及主题编号
2001 - 2006	1_T0 planning technology \| 1_T1 behavior simulation \| 1_T2 decision-making \| 1_T3 medical diagnosis \| 1_T4 pattern recognition \| 1_T5 case-based reasoning \| 1_T6 genetic algorithm \| 1_T7 decision support system \| 1_T8 machine-learning \| 1_T9 fuzzy-logic \| 1_T10 neural network \| 1_T11 robot \| 1_T12 intelligent agent \| 1_T13 process control \| 1_T14 mathematical computing
2007 - 2011	2_T0 forecast \| 2_T1 data mining \| 2_T2 machine learning \| 2_T3 logic reasoning \| 2_T4 decision making \| 2_T5 feature extraction \| 2_T6 support vector machine \| 2_T7 genetic algorithm \| 2_T8 intelligent agent \| 2_T9 neural network prediction \| 2_T10 process control \| 2_T11 information retrieval \| 2_T12 robotics \| 2_T13 artificial neural network \| 2_T14 human
2012 - 2016	3_T0 risk evaluation \| 3_T1 forecast \| 3_T2 resource management \| 3_T3 case-based reasoning \| 3_T4 random forest \| 3_T5 neural network \| 3_T6 mathematical model \| 3_T7 hierarchical database \| 3_T8 big data \| 3_T9 classification \| 3_T10 decision trees \| 3_T11 fuzzy logic \| 3_T12 neuro-fuzzy inference system \| 3_T13 simulation \| 3_T14 industrial robots \| 3_T15 intelligent voice \| 3_T16 computational complexity \| 3_T17 intelligent agent \| 3_T18 human-computer interaction \| 3_T19 image processing \| 3_T20 optimization algorithm \| 3_T21 time-real monitoring \| 3_T22 field application \| 3_T23 robot
2017 - 2020	4_T0 smart wearable device \| 4_T1 particle swarm optimization \| 4_T2 evolutionary computation \| 4_T3 intelligence manufacturing \| 4_T4 internet of things \| 4_T5 virtual reality \| 4_T6 remote diagnosis \| 4_T7 neuro-fuzzy inference system \| 4_T8 security \| 4_T9 feature extraction \| 4_T10 motion control \| 4_T11 medical image processing \| 4_T12 intelligent life \| 4_T13 transfer learning \| 4_T14 computational complexity \| 4_T15 object tracking \| 4_T16 big data \| 4_T17 social network \| 4_T18 representation learning \| 4_T19 neural network \| 4_T20 reinforcement learning \| 4_T21 machine learning \| 4_T22 information retrieval \| 4_T23 machine translation \| 4_T24 disease diagnosis \| 4_T25 knowledge graph \| 4_T26 decision-making \| 4_T27 forecasting \| 4_T28 computational complexity \| 4_T29 natural language processing \| 4_T30 distributed system \| 4_T31 time series model \| 4_T32 deep neural network \| 4_T33 robotics \| 4_T34 anomaly detection \| 4_T35 question & answering \| 4_T36 3D \| 4_T37 ontology \| 4_T38 pattern recognition \| 4_T39 fuzzy evaluation \| 4_T40 information retrieval \| 4_T41 clustering

Topic Recognition Results in Each Time Period

Topic Evolution Path

Path Evolution Diagram of Higher Topic Importance

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