IEEE Access (Jan 2024)

Design of Intelligent Sentiment Classification Model Based on Deep Neural Network Algorithm in Social Media

  • Qingxiang Zeng

DOI
https://doi.org/10.1109/ACCESS.2024.3409818
Journal volume & issue
Vol. 12
pp. 81047 – 81056

Abstract

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Aspect-based sentiment classification, as a more fine-grained sentiment analysis task, focuses on predicting the sentiment tendency expressed in a sentence based on specific aspects. However, current text sentiment analysis models face challenges when dealing with long comments posted by users on social media, as users often do not explicitly mention sentiment aspects directly in their comments. This paper focuses on aspect extraction and sentiment classification. By constructing a neural network model that integrates a self-attention mechanism, the model is able to learn word embeddings that incorporate contextual semantic information. Furthermore, the author introduce a self-attention mechanism based on relative position representations, which aims to simulate the order of words and achieve parallelized training of inputs by reducing parameters, while simultaneously extracting aspect and sentiment features. Additionally, the author designed a convolutional neural network model and utilized the ReLu gate to selectively output sentiment features based on the given aspect category, while implementing the K-max pooling technique. Comparative experiments conducted on three standard datasets, SemEval, Tweets, and CVAT, showed that this model achieved average best performance on all three datasets. Specifically, on the SemEval dataset, when predicting valence values, the MSE, MAE, and Pearson correlation coefficient reached optimal values of 1.00, 0.88, and 0.73, respectively. While the overall performance on the CVAT dataset was slightly lower, this model still achieved the best MSE of 0.89, MAE of 0.81, and Pearson correlation coefficient of 0.64 when predicting arousal values. This result demonstrates that this method provides relatively balanced and excellent performance in predicting both valence and arousal, validating its practical application value in the field of sentiment analysis.

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