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

PARK HO YEON (박호연); Kyoung-jae Kim (김경재)

doi:10.9708/jksci.2020.25.08.181

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

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2020, 25(8), pp.181-188
DOI : 10.9708/jksci.2020.25.08.181
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : July 28, 2020
Accepted : August 11, 2020
Published : August 31, 2020

PARK HO YEON ¹, Kyoung-jae Kim ¹

¹동국대학교

Accredited

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.

KEYWORDS

sentiment analysis, Bag of words, TF-IDF, Word2Vec, machine learning

Citation status

* References for papers published after 2023 are currently being built.

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This paper was written with support from the National Research Foundation of Korea.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

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

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KEYWORDS

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* References for papers published after 2023 are currently being built.