Authors:
Fumiyo Fukumoto
1
;
Yoshimi Suzuki
1
;
Akihiro Nonaka
1
and
Karman Chan
2
Affiliations:
1
Univ. of Yamanashi, Japan
;
2
IIJ Innovation Institute Inc., Japan
Keyword(s):
Publication Statistics, Sentiment Analysis, Prediction, Company’s Trend.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Intelligence Applications
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
This paper presents a method for predicting company’s trend on research and development(R&D) in business
area. We used three types of data collections, i.e, scientific papers, open patents, and newspaper articles to
estimate temporal changes of trends on company’s business area. We used frequency counts on scientific
papers and open patents to be published in time series. For news articles, we applied sentiment analysis to
extract positive news reports related to the company’s business areas, and count their frequencies. For each
company, we then created temporal changes based on these frequency statistics. For each business area, we
clustered these temporal changes. Finally, we estimated prediction models for each cluster. The results show
that the the model obtained by combining three data is effective to predict company’s future trends, especially
the results show that SP clustering contributes overall performance.