## 2 Related Work  
The analysis of customer reviews has become a prominent area of research, as indicated by the increasing volume of academic work in this domain. In recent years, there has been a surge of interest in ATE, particularly within the frameworks of supervised, unsupervised, and hybrid learning [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. 1. Supervised aspect extraction utilizes annotated datasets to teach machine learning algorithms to automatically recognize and extract aspect terms from customer reviews related to products or services [12; 13; 14; 15; 16; 17; 18]. [PERSON] et al. [12] proposed a Multi-Task Neural Network (MTNN) framework for ATE, integrating Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Networks (CNN) to improve multi-task learning outcomes using restaurant review data. [PERSON] et al. [13] introduced the Global Inference (GI) approach for ATE applied to MTNN. Their method integrates various syntactic elements to explicitly capture intra-aspect and inter-aspect relationships. [PERSON] et al. [14] introduced a language-agnostic system for aspect extraction that combines local, surface-level attributes with Semantic Distributional Features based on Clustering (SDFC) to aspect identification. [PERSON] et al. [15] present an innovative framework for ATE. This framework utilizes a Bidirectional Dependency Tree Network (BiDTree) to capture tree-structured relationships within sentences. The embeddings learned from the BiDTree are subsequently fused with a Conditional Random Field (CRF) to leverage sequential information. This BiDTreeCRF approach enables the model to concurrently learn syntactic and sequential features, resulting in enhanced performance of the ATE. [PERSON] et al. [16] present a Bi-LSTM paired with a self-attention mechanism for ATE. This strategy employs Bi-LSTMs to capture sequential information within sentences, and the self-attention mechanism enables the model to concentrate on the most pertinent segments of the input for ATE. [PERSON] and [PERSON] [17] introduced a new Seq2 Seq learning framework for ATE using the Information-Augmented Neural Network (IANN), which utilizes the foundation of a neural network. This model addresses the issue of the limited capacity of static word embedding used in ATE, which are insufficient for capturing the evolving meaning of words. [PERSON] and [PERSON]. [18] proposed a new extraction model called BiLSTM-BGAT-GCN, which is used to identify aspect terms, opinion terms, and their corresponding sentiment orientations in customer review. 2. Unsupervised aspect extraction involves automatically identifying and extracting aspects from customer reviews without relying on pre-labeled training data. [PERSON] et al. [19] proposed a model for Chinese-oriented aspect-based sentiment analysis called LCF-ATEPC. This model utilizes the BERT model to improve the performance of ATE. Additionally, the methodologies employed in this task encompass the following techniques [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]: 1. Frequency-based ATE approaches utilize the distribution of word or phrase frequencies within a text to discern significant aspects based on their frequent occurrence [20; 21; 22]. [PERSON] and [PERSON] [20] introduced a technique for aspect extraction from customer reviews, employing the frequency distribution of aspects within the reviews. [PERSON] et al. [21] presented their method for ATE from customer reviews, which exploits frequency distribution to identify recurring aspects discussed by customers. [PERSON] and [PERSON]. [22] introduced a two-fold rule-based model for aspect extraction called TF-RBM, which relies on rules derived from sequential patterns extracted from customer reviews. Moreover, this study has implemented frequency and similarity-based techniques to enhance the aspect extraction accuracy of the proposed model. 2.