Performance Comparison of Sentiment Lexicons in Predicting American Citizen's Sentiment for the Apartment Rents
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The primary purpose of this study is to identify which sentiment lexicon performs better in predicting citizens' sentiment about apartment rents. To accomplish this research purpose, I performed sentiment analysis based on sentiment lexicon and sentiment analysis based on the machine learning algorithm simultaneously. As a result of the analysis, the AFINN Sentiment Lexicon turned out to be the best performing sentiment lexicon in all performance metrics categories. Also, the performance of each machine learning classifier showed a significant difference in classifying and predicting citizens' sentiment on apartment rents by sentiment lexicon. In other words, AdaBoost in the AFINN Sentiment Lexicon and the Opinion Lexicon and Random Forest in the NRC Emotion Lexicon proved to be the best algorithms. This study can contribute to the research of this area because it uses sentiment analysis based on the sentiment lexicon and sentiment analysis based on the machine learning algorithm simultaneously in classifying and predicting citizens' sentiment about apartment rents. Despite these advantages, this research has limitations in that it uses only a limited range of sentiment lexicons and machine learning algorithms. Therefore, future studies will have to use more sentiment lexicons and machine learning algorithms to classify and predict citizens' sentiment about apartment rents accurately.
Abstract
The primary purpose of this study is to identify which sentiment lexicon performs better in predicting citizens' sentiment about apartment rents. To accomplish this research purpose, I performed sentiment analysis based on sentiment lexicon and sentiment analysis based on the machine learning algorithm simultaneously. As a result of the analysis, the AFINN Sentiment Lexicon turned out to be the best performing sentiment lexicon in all performance metrics categories. Also, the performance of each machine learning classifier showed a significant difference in classifying and predicting citizens' sentiment on apartment rents by sentiment lexicon. In other words, AdaBoost in the AFINN Sentiment Lexicon and the Opinion Lexicon and Random Forest in the NRC Emotion Lexicon proved to be the best algorithms. This study can contribute to the research of this area because it uses sentiment analysis based on the sentiment lexicon and sentiment analysis based on the machine learning algorithm simultaneously in classifying and predicting citizens' sentiment about apartment rents. Despite these advantages, this research has limitations in that it uses only a limited range of sentiment lexicons and machine learning algorithms. Therefore, future studies will have to use more sentiment lexicons and machine learning algorithms to classify and predict citizens' sentiment about apartment rents accurately.
참고문헌 (48)
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