###### Abstract  
Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the online shopping10 cots dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively. natural language processing; sentiment classification; BiLSTM; self-attention; scalable multi-channel +
Footnote †: journal: _Future Internet_**2023**, _15_, 364. [[https://doi.org/10.3390/fi5110364](https://doi.org/10.3390/fi5110364)]([https://doi.org/10.3390/fi5110364](https://doi.org/10.3390/fi5110364))
during the training process. Three traditional machine learning models commonly used in sentiment analysis are SVM (Support Vector Machine) [8], NB (Naive Bayes) [9; 10], and KNN (K-Nearest Neighbors) [11; 12]. [PERSON] [13] conducted a comparative analysis of SVM and NB methods and found that when the dataset is large enough, Naive Bayes achieves higher accuracy than SVM. [PERSON] et al. [14] applied KNN and SVM for sentiment polarity identification in Twitter texts. In KNN, they classified texts based on the Euclidean distance measured between them, while SVM used principal component analysis to classify sentiment labels. Due to the low dimensionality of the experiment, it was difficult to determine the hyperplane in SVM, making KNN more effective in classifying positive and negative emotions. Machine learning, benefiting from a larger training dataset, provides more accurate sentiment analysis with better scalability and reproducibility. However, machine learning-based text sentiment classification methods still have some limitations compared to deep learning. Higher accuracy relies on high-quality training datasets, which require costly manual labeling. Moreover, subjective data labeling can impact the final classification performance. Therefore, model tuning and optimization are necessary to enhance the robustness and generalization ability of the model. In the domain of natural language processing (NLP), LSTM (Long Short-Term Memory) has been widely used in sentiment analysis. LSTM is a special type of recurrent neural network (RNN) that effectively captures semantic information in text and addresses the issue of long-term dependencies. However, LSTM can only infer future information based on the preceding context. In Chinese language environments, BiLSTM (Bidirectional Long Short-Term Memory) [15] is employed to both consider contextual aspects and capture bidirectional semantic dependencies, thus improving the accuracy of sentiment analysis models. To enhance the accuracy of sequence models, the Attention mechanism was introduced by [PERSON] et al. in 2015 [16]. The core idea is to establish a fuzzy cognition at a macroscopic level and assign higher weights to more important features within the sequence. This mechanism leads to significant improvements in the performance of sequence models. In 2017, [PERSON] et al. [17] further extended the Attention mechanism to propose Self-Attention.