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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

ISBN: 978-989-758-203-5

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.

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Paper citation in several formats:
Fukumoto, F.; Suzuki, Y.; Nonaka, A. and Chan, K. (2016). Prediction of Company's Trend based on Publication Statistics and Sentiment Analysis.In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 283-290. DOI: 10.5220/0006048602830290

@conference{kdir16,
author={Fumiyo Fukumoto. and Yoshimi Suzuki. and Akihiro Nonaka. and Karman Chan.},
title={Prediction of Company's Trend based on Publication Statistics and Sentiment Analysis},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={283-290},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006048602830290},
isbn={978-989-758-203-5},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Prediction of Company's Trend based on Publication Statistics and Sentiment Analysis
SN - 978-989-758-203-5
AU - Fukumoto, F.
AU - Suzuki, Y.
AU - Nonaka, A.
AU - Chan, K.
PY - 2016
SP - 283
EP - 290
DO - 10.5220/0006048602830290

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