Research on Emotional Analysis of Online Book Reviews Based on Word2Vec Method
DOI:
https://doi.org/10.54097/fbem.v11i1.12038Keywords:
Enter key words or phrases in alphabetical order, Separated by commas.Abstract
At present, emotional analysis in the field of natural language processing is gradually becoming a popular research direction on social media. With the rise of artificial intelligence technology and deep learning algorithms, word vector representation methods based on neural networks have emerged and emerged in the field of sentiment analysis. According to the current research results of experts, the Word2Vec model has become the mainstream word vector representation method. The algorithm model is based on continuous optimization and iteration of random initialization word vectors, ultimately obtaining a stable performance word vector representation. If the research goal is to obtain sentence vectors, weighting processing is also required. Therefore, after systematically summarizing and analyzing the representation techniques of initialized word vectors and solving sentence vectors through word vector weighting, this article proposes corresponding improvement methods. This article improves the initialization algorithm of its word vector based on the Word2Vec model. On the basis of the original randomly generated word vector, the correlation information of words is added to change the distribution characteristics of the initial word vector, thereby enhancing the performance of the word vector. In the comparative experiment of the user comment dataset, the improved model showed a 1% to 3% improvement in sentiment polarity classification accuracy compared to the other model.
Downloads
References
Dharma, Eddy Muntina, et al, “The accuracy comparison among word2vec, glove, and fasttext towards convolution neural network (cnn) text classification,” J Theor Appl Inf Technol, vol. 100, no. 2, pp. 31, Jan 2022.
Zhu, Jun-Jie, Zhiyong Jason Ren, "The evolution of research in resources, conservation & recycling revealed by Word2vec-enhanced data mining." Resources, Conservation and Recycling, vol. 190, pp. 106876, 2023.
Styawati, Styawati, Andi Nurkholis, Ahmad Ari Aldino, Selamet Samsugi, Emi Suryati, and Ryan Puji Cahyono. "Sentiment analysis on online transportation reviews using Word2Vec text embedding model feature extraction and support vector machine (SVM) algorithm." In 2021 International Seminar on Machine Learning, Optimization, and Data Science (ISMODE), pp. 163-167. IEEE, 2022.
Ma, Jian, et al. "An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to local," Expert Systems with Applications, vol. 212, pp. 118695, 2023.
Y. Gao, "Research and Application of Emotional Analysis Based on Word2Vec Method (Master's Thesis," Xiamen University, 2019.
M. Zhou, "Application of sentiment analysis based on LSTM and Word2Vec in product reviews, " Statistics and Management, vol. 12, pp.81-84, 2019.
X. Yan, "A Study on Emotional Analysis of Weibo Stock Review Text Based on Word2vec and LSTM," Nanjing University, 2018.
X. B. Peng, "Research on Text Emotion Analysis Method Based on Word2vec," Network Security Technology and Application, vol. 07, pp.58-59, 2016.
M. B. Li, W. Wang, C. C. Wang, "Exploring the Emotional Analysis Method for Online Teaching Evaluation in Higher Vocational Education Based on Word2Vec and Bi GRU, " Journal of Guangdong Water Resources and Electric Power Vocational and Technical College, vol. 21, no. 03, pp. 58-59, 2023.
Y. Z. Wang, T. Wang, "Research on Emotional Analysis Method Based on Weighted Word2Vec XGBoost, " Journal of Chongqing University of Science and Technology (Natural Science Edition), vol. 25, no. 03, pp. 62-66, 2023.
M. Zhou, "Application of sentiment analysis based on LSTM and Word2Vec in product reviews," Statistics and Management, vol. 12, pp. 81-84, 2019.
