We are conducting sentiment analysis and popularity prediction of multimedia contents. We are also studying classification and retrieval methods for data in specific domains such as map images and fashion images.
- Classification of map images [Sawada&Katsurai, ICASSP2020]
- Popularity prediction of recipe images [Sanjo&Katsurai, CIKM2017]
- Image sentiment analysis [Katsurai&Satoh, ICASSP2016]
We propose a method for automatically assigning emotion scores to words not included in general dictionaries (e.g. pictographs and slangs).
- Emoji sentiment dictionary [Kimura&Katsurai, FAB2017(ASONAM2017)]
- omparison of Emoji between Japanese and English tweets [Kimura&Katsurai, iiWAS2018]
Relating data on the web to each other is a fundamental technique for knowledge discovery and machine learning. We are currently working on the automatic linking of records between different databases.
- Multilingual author matching [Chikazawa, Katsurai, Ohmukai, Scientometrics, 2021]
- Author matching [Katsurai&Ohmukai, JCDL2019]
Researcher Topic Analysis
The system estimates the researcher’s expert topic from the text of the article title, keywords, and abstract, and applies it to the search of related researchers and author identification.
- Researcher search system [Takahashi, Tango, Chikazawa, &Katsurai, ICADL2020]
- Extraction of researchers’ topics [Katsurai+, IEICETrans, 2016]
Social Network Analysis
Social networks represent the structure of collaboration in intellectual creative activities. We analyze the activity within communities and the differences between communities.
- Analysis of the current state of collaboration within research institutions [Araki, Katsurai+, IEICE Trans, 2017]
Knowing people’s interests helps in strategy development and marketing. We analyze large amounts of data to extract and visualize prevailing topics.
- Research trend visualization [Katsurai&Ono, Scientometrics, 2019]