Mr. Albert A. Vinluan Profile

Albert A. Vinluan

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Proceedings Article | 17 October 2023 Paper

Emotional analysis and prediction based on online book user comments

Jingxiu Xu, Albert Vinluan

Proceedings Volume 12803, 128030M (2023) https://doi.org/10.1117/12.3009554

KEYWORDS: Data modeling, Machine learning, Emotion, Education and training, Analytical research, Integrated modeling, Decision trees, Feature extraction, Data analysis, Lawrencium

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With the continuous development of information technology, more and more scholars use machine learning and integrated learning algorithms to analyze and predict the text emotion of online text reviews, explore the emotional tendencies of online users, which has far-reaching theoretical guiding significance and practical value for the promotion of online book sales decisions in the later stage. In this paper, user emotion is mined and analyzed based on online book reviews. The user emotion tendency is obtained through the emotion analysis method, which can help the e-commerce understand the user preferences and the quality of books on time, recommend books according to the user preferences, and play an auxiliary role in decision-making for consumers to purchase. Because of the current single method model and the inability to compare the accuracy of emotional prediction, this paper uses the public dataset Book_review on the official website of Kaggle, using NLTK (Nature Language TooKit), a natural language processing tool, to clean text data, build machine learning models and integrated learning models based on n-gram text features, obtain the results of emotional prediction through experimental analysis, and compare the machine learning model with the integrated learning model method, selecting the Logistic Regression model with the highest accuracy for emotional analysis of online book reviews, identify users’ emotional tendencies. Finally, we found that the accuracy of online comment emotion prediction based on the n-gram text feature Logistic Regression model is about 5% higher than the existing research methods.

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