디지털융복합연구 (Journal of Digital Convergence)

  • 제17권2호
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  • Pages.163-170
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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속성선택방법과 워드임베딩 및 BOW (Bag-of-Words)를 결합한 오피니언 마이닝 성과에 관한 연구

Investigating Opinion Mining Performance by Combining Feature Selection Methods with Word Embedding and BOW (Bag-of-Words)

  • 어균선 (성균관대학교 경영대학) ;
  • 이건창 (성균관대학교 글로벌경영학과/삼성융합의과학원 융합의과학과)
  • Eo, Kyun Sun (SKK Business School, Sungkyunkwan University) ;
  • Lee, Kun Chang (Global Business Administration/Dept of Health Sciences & & Technology, SHAIHST Sungkyunkwan University)
  • 투고 : 2018.11.13
  • 심사 : 2019.02.20
  • 발행 : 2019.02.28
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초록

과거 10년은 웹의 발달로 인한 데이터가 폭발적으로 생성되었다. 데이터마이닝에서는 대용량의 데이터에서 무의미한 데이터를 구분하고 가치 있는 데이터를 추출하는 단계가 중요한 부분을 차지한다. 본 연구는 감성분석을 위한 재표현 방법과 속성선택 방법을 적용한 오피니언 마이닝 모델을 제안한다. 본 연구에서 사용한 재표현 방법은 백 오즈 워즈(Bag-of-words)와 Word embedding to vector(Word2vec)이다. 속성선택(Feature selection) 방법은 상관관계 기반 속성선택(Correlation based feature selection), 정보획득 속성선택(Information gain)을 사용했다. 본 연구에서 사용한 분류기는 로지스틱 회귀분석(Logistic regression), 인공신경망(Neural network), 나이브 베이지안 네트워크(naive Bayesian network), 랜덤포레스트(Random forest), 랜덤서브스페이스(Random subspace), 스태킹(Stacking)이다. 실증분석 결과, electronics, kitchen 데이터 셋에서는 백 오즈 워즈의 정보획득 속성선택의 로지스틱 회귀분석과 스태킹이 높은 성능을 나타냄을 확인했다. laptop, restaurant 데이터 셋은 Word2vec의 정보획득 속성선택을 적용한 랜덤포레스트가 가장 높은 성능을 나타내는 조합이라는 것을 확인했다. 다음과 같은 결과는 오피니언 마이닝 모델 구축에 있어서 모델의 성능을 향상시킬 수 있음을 나타낸다.

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

키워드

DJTJBT_2019_v17n2_163_f0001.png 이미지

Fig. 1. Word2vec

DJTJBT_2019_v17n2_163_f0002.png 이미지

Fig. 2. Procedures

Table 1. Opinion mining studies

DJTJBT_2019_v17n2_163_t0001.png 이미지

Table 2. BOW results

DJTJBT_2019_v17n2_163_t0002.png 이미지

Table 3. WE results

DJTJBT_2019_v17n2_163_t0003.png 이미지

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