Text Analysis
Text analysis + NLP
The increasing availability of large volumes of text data has spurred the development of natural language processing (NLP) techniques for extracting useful information from unstructured text. NLP has been applied to various fields, including sentiment analysis, topic modeling, and entity recognition, among others. While these techniques can reveal valuable insights, they often produce large and complex outputs, which can be difficult to interpret and analyze.
To overcome these challenges, there has been growing interest in combining NLP with text visualization techniques to create a more intuitive representation of the data. Text visualization is the process of representing text data visually to enable more effective exploration and interpretation. By combining NLP with text visualization, researchers can analyze large volumes of text data more efficiently and gain a better understanding of the underlying trends and patterns.
Publications:
Yamei Tu, Rui Qiu, Yu-Shuen Wang, Po-Yin Yen, and Han-Wei Shen. "PhraseMap: Attention-Based Keyphrases Recommendation for Information Seeking." IEEE Transactions on Visualization and Computer Graphics (2022).
Rui Qiu, Yamei Tu, Yu-Shuen Wang, Po-Yin Yen, Han-Wei Shen: DocFlow: A Visual Analytics System for Question-based Document Retrieval and Categorization. IEEE Transactions on Visualization and Computer Graphics 2022
Yamei Tu, Jiayi Xu, Han-Wei Shen: KeywordMap: Attention-based Visual Exploration for Keyword Analysis, Pacific Visualization Symposium (PacificVis), 2021 IEEE, 206-215
Xiaonan Ji, Yamei Tu, Wenbin He, Junpeng Wang, Han-Wei Shen, and Po-Yin Yen: USEVis: Visual analytics of attention-based neural embedding in information retrieval, Visual Informatics (2021).
Xiaonan Ji, Han-Wei Shen, Raghu Machiraju, Alan Ritter, and Po-Yin Yen: Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection, IEEE Transactions on Visualization and Computer Graphics 25 (6), 2181-2192