Impact of Word Embedding Methods on Performance of Sentiment Analysis with Machine Learning Techniques

다국어 초록 (Multilingual Abstract)

In this study, we propose a comparative study to confirm the impact of various word embedding techniques on the performance of sentiment analysis. Sentiment analysis is one of opinion mining techniques to identify and extract subjective information from text using natural language processing and can be used to classify the sentiment of product reviews or comments. Since sentiment can be classified as either positive or negative, it can be considered one of the general classification problems. For sentiment analysis, the text must be converted into a language that can be recognized by a computer. Therefore, text such as a word or document is transformed into a vector in natural language processing called word embedding. Various techniques, such as Bag of Words, TF-IDF, and Word2Vec are used as word embedding techniques. Until now, there have not been many studies on word embedding techniques suitable for emotional analysis. In this study, among various word embedding techniques, Bag of Words, TF-IDF, and Word2Vec are used to compare and analyze the performance of movie review sentiment analysis. The research data set for this study is the IMDB data set, which is widely used in text mining. As a result, it was found that the performance of TF-IDF and Bag of Words was superior to that of Word2Vec and TF-IDF performed better than Bag of Words, but the difference was not very significant.

참고문헌 (Reference)

1 성대경, "딥러닝을 활용한 정치 기사 댓글 분석" 한국컴퓨터정보학회 23 (23): 9-15, 2018

2 박진규, "딥러닝 기법을 이용한 철도 운행 관련 비정형 SNS 메시지 정형화" 한국컴퓨터정보학회 23 (23): 19-26, 2018

3 김유희, "딥러닝 기반 기사단위 및 문단 단위별 분류" 한국컴퓨터정보학회 23 (23): 31-41, 2018

4 김우주, "Word2vec을 활용한 문서의 의미 확장 검색방법" 한국콘텐츠학회 16 (16): 687-692, 2016

5 이태일, "Word2Vec과 가속화 계층적 밀집도 기반 클러스터링을 활용한 효율적 봇넷 탐지 기법" 한국인터넷정보학회 20 (20): 11-20, 2019

6 J. Read, "Using emoticons to reduce dependency in machine learning techniques for sentiment classification" 43-48, 2005

7 B. Pang, "Thumbs up? Sentiment classification using machine learning techniques" 79-86, 2002

8 Q. T. Ain, "Sentiment analysis using deep learning techniques : a review" 8 (8): 424-433, 2017

9 G. Gautam, "Sentiment analysis of twitter data using machine learning approaches and semantic analysis" 437-442, 2014

10 L. Dey, "Sentiment analysis of review datasets using Naive Bayes and k-nn classifier" 8 (8): 54-62, 2016

11 C. Bhadane, "Sentiment analysis : Measuring opinions" 45 (45): 808-814, 2015

12 F. H. Khan, "SentiMI : Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection" 39 : 140-153, 2016

13 T. Mikolov, "Distributed representations of words and phrases and their compositionality" 3111-3119, 2013

14 A. Abdi, "Deep learning-based sentiment classification of evaluative text based on Multi-feature fusion" 56 (56): 1245-1259, 2019

15 T. A. Rana, "Aspect extraction in sentiment analysis : comparative analysis and survey" 46 (46): 459-483, 2016

16 F. Tang, "Aspect based fine-grained sentiment analysis for online reviews" 488 : 190-204, 2019

17 S. M. Liu, "A multi-label classification based approach for sentiment classification" 42 (42): 1083-1093, 2015