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Improvement Method for Topic-Based Path Model by Using Word2vec

  • Ryosuke Saga
  • Shoji Nohara
Conference paper

Abstract

Studying purchasing factor for product developers in the market place is important. Using text data, such as comments from consumers, for factor analysis is a valid method. However, previous research show that generating a stable model for factor analysis using text data is difficult. We assume that if the target text data are handled well, then the analysis can progress smoothly. This study proposes pre-processing text data by word2vec for factor analysis to improve the analysis. Word2vec regards words as vectors in text. Our proposed process is effective, because variables are expressed as the frequency of words in the analysis model. Experiment results also show that our proposed method is helpful in generating an analytical model.

Keywords

Causal analysis Data ming Text mining Topic model Structural equation modeling Word2vec 

Notes

Acknowledgements

This work was supported by KAKENHI 25240049.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Graduate School of Humanities and Sustainable System SciencesOsaka Prefecture UniversitySakaiJapan
  2. 2.Graduate School of EngineeringOsaka Prefecture UnivesritySakaiJapan

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