## 1 Introduction  
Electronic commerce plays a vital role in facilitating transactions over the Internet, encompassing the exchange of goods and services [1, 2]. Consumer reliance on product reviews, given by previous customers, is substantial, as they serve as crucial aids in purchasing decisions. Concurrently, entrepreneurs leverage customer feedback to enhance their offerings. However, condensing and synthesizing these reviews present significant challenges, necessitating the application of techniques such as Sentiment Analysis (SA) [3, 4, 5]. SA categorizes sentences as positive, negative, or neutral based on sentiment polarity extracted from Natural Language Processing (NLP) [5]. NLP is a subfield of Artificial Intelligence (AI) that employs machine learning techniques to facilitate computers in comprehending and communicating with human language [6]. However, SA primarily captures the overall sentiment in customer reviews without targeting specific aspects. Aspect-Based Sentiment Analysis (ABSA) [7, 8] addresses this limitation by focusing on evaluating customer feedback and measuring satisfaction with products or services [8]. ABSA comprises three stages: (1) Aspect Term Extraction (ATE): extracts aspect terms from individual reviews. (2) Aspect Categorization Extraction (ACE): identifies and extracts aspect categories from customer reviews. (3) SA: Determines the polarity. Examples of extracted aspect terms include \"spicy roll\", \"waiter\", and \"decor\", while examples of aspect categories for each aspect term are \"food\", \"service\", and \"ambiance\", respectively. Therefore, the primary objective of this research is to concentrate on extracting aspect terms. In recent years, ATE has been approached primarily through two methods. The lexicon-based approach involves creating an aspect lexicon that is integrated into a static lexicon, which is a predetermined list of words [9; 10; 11]. For instance, [PERSON] et al. [9] and [PERSON] et al. [10] utilized aspect lists derived from datasets to identify aspect terms, while [PERSON] et al. [11] developed an aspect term lexicon in combination with specific rules. Conversely, the machine learning approach has gained popularity in ATE, relying on datasets for aspect training and extraction employing supervised, unsupervised, and hybrid learning methods [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; 42; 43; 44]. [PERSON] and [PERSON]. [42] introduced the Bidirectional Encoder Representations from Transformers (BERT), a knowledge-enhanced language representation model, for aspect-based sentiment analysis. This model belongs to the unsupervised methods. In order to achieve comprehensible and precise identification of aspect terms and their accompanying aspect-sentiment detection, it is beneficial to incorporate external domain-specific knowledge. [PERSON]. [43] proposed a new framework called Aspect Sentiment Quadruplet Extraction (ASQE). This research goal is to identify aspect terms using the Label-Semantics Enhanced Multi-Layer Heterogeneous Graph Convolutional Network (LSEMH-GCN), which falls under the category of supervised methods. [PERSON] and [PERSON]. [44] proposed a hybrid approach to aspect-based sentiment analysis, which integrates domain lexicons and rules, to analyze entity smart app reviews using data from smart government reviews. However, despite advancements in ATE research, the literature on this topic faces several challenges:  
1. Identifying aspect terms presents challenges, especially when dealing with multiple words. It is imperative to conduct a comprehensive analysis, particularly for multiple-word aspect terms not presented in the training dataset. For instance, if the term \"views of city\" is absent from the training data, it may not be recognized as an aspect, despite the presence of the term \"views\" alone. 2. Relying solely on the training dataset for aspect extraction proves insufficient, particularly when certain aspects or semantic nuances are not represented.