.png){kind=icon}
返回
Modeling Topic Evolution in Twitter: An Embedding-Based Approach
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2018.2878494.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In last two decades, online social networks have grown vertically as well as horizontally. Due to various users activities in these networks, huge amount of data, mainly textual, is being generated that can be analyzed at different levels of granularity for various purposes, including behavior analysis, sentiment analysis, and predictive modeling. In this paper, we propose a word embedding-based approach to analyze users-centric tweets to observe their behavior evolution in terms of the topics discussed by them over a period of time. We also present a word embedding-based proximity measure to monitor temporal transitions between the topics using five topic evolution events - emergence, persistence, convergence, divergence, and extinction. The proximity between a pair of topics is defined as a function of the content and contextual similarity between their word distributions, wherein the contextual similarity is calculated using word embedding. The proposed approach is evaluated over three Twitter datasets in line with the existing state-of-the-art approaches in literature and the experimental results are encouraging.
Keyword:
Social network analysis
twitter data analysis
temporal evolution
topic modeling
word embedding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
分享收藏
Cobalt and Copper Composite Oxides as Efficient Catalysts for Preferential Oxidation of CO in H2-Rich Stream钴和铜复合氧化物作为H2-Rich流中CO优先氧化的有效催化剂
PREAI
分享收藏 没有更多内容
没有更多内容