Microblog Negative Comments Data Analysis Model Based on Multi-scale Convolutional Neural Network and Weighted Naive Bayes Algorithm

• Published in: Neural processing letters Vol. 56; no. 5; p. 229
• Main Authors: Zhou, Chunliang, Meng, XiangPei, Shen, Zhaoqiang
• Format: Journal Article
• Language: English
• Published: New York Springer US 05.09.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Artificial neural networks; Categories; Classification; Clustering; Complex Systems; Computational Intelligence; Computer Science; Convergence; Data analysis; Dictionaries; Libraries; Machine learning; Microblogs; Neural networks; Probability; Public opinion; Sentiment analysis; Social networks; User behavior; Weighted Naive Bayes; Influence on public opinion; Multi-scale convolutional neural network; Sentiment classification
• ISSN: 1573-773X; 1370-4621; 1573-773X
• DOI: 10.1007/s11063-024-11688-9

As a form of public supervision, Microblog’s negative reviews allow people to share their opinions and experiences and express dissatisfaction with unfair and unreasonable phenomena. This form of supervision has the potential to promote social fairness, drive governments, businesses, and individuals to correct mistakes and enhance transparency. To characterize the sentiment trend and determine the influence of Microblog negative reviews, we propose a multi-scale convolutional neural network and weighted naive bayes algorithm (MCNN–WNB). We define the feature vector characterization index for Microblog negative review data and preprocess the data accordingly. We quantify the relationship between attributes and categories using the weighted Naive Bayes method and use the quantification value as the weighting coefficient for the attributes, addressing the issue of decreased classification performance in traditional methods. We introduce a sentiment classification model based on word vector representation and a multi-scale convolutional neural networks to filter out Microblog negative review data. We conduct simulation experiments using real data, analyzing key influencing parameters such as convergence time, training set sample size, and number of categories. By comparing with K-means, Naive Bayes algorithm, Spectral Clustering algorithm and Autoencoder algorithm, we validate the effectiveness of our proposed method. We discover that the convergence time of the MCNN–WNB algorithm increases as the number of categories increases. The average classification accuracy of the algorithm remains relatively stable with varying test iterations. The algorithm’s precision increases with the number of training set samples and eventually stabilizes.