## 2 Related Work
Syntax-based ATE approaches utilize the grammatical structure of sentences to identify aspect terms, often employing part-of-speech (POS) tagging, dependency parsing, and predefined syntactic patterns [23; 24; 25; 26; 27]. [PERSON] et al. [23] introduced a novel approach for ATE from product reviews, which utilizes generalized syntactic patterns and similarity measures to improve ATE performance. [PERSON] et al. [24] introduce a new approach to ATE based on unstructured reviews. They suggest a rule-based system that utilizes syntactic patterns derived from feature observations within the review text. This method is supported by a comprehensive analysis of the diverse patterns identified in the data. [PERSON] et al. [25] assessed the effectiveness of different dependency relations in combination with various POS tagging patterns for candidate ATE from customer reviews. [PERSON] et al. [26] introduce an opinion monitoring service that utilizes dependency parsing for ATE and integrates a visualization tool to assist in data monitoring. [PERSON] et al. [27] presents an adaptive semantic relative distance method that relies on dependent syntactic analysis. It utilizes adaptive semantic relative distance to identify the suitable local context for each text, hence enhancing the precision of sentiment analysis. 3. Topic modeling is an unsupervised statistical technique used to reveal latent thematic structures within a set of documents. One of the most widely used and well-established algorithms for topic modeling is Latent Dirichlet Allocation (LDA) [50]. LDA is a statistical model that represents documents as combinations of a predetermined number of underlying topics. Each topic is defined by a probability distribution over the vocabulary, indicating the likelihood of each word appearing within that topic [51]. LDA has been applied to ATE in the realm of ABSA, as discussed in several studies [28; 29; 30; 31; 32; 33; 34]. [PERSON] and [PERSON] [28] introduced Multi-grain Latent Dirichlet Allocation (MG-LDA), an extension of LDA, for joint topic modeling and ATE from online customer reviews. MG-LDA captures aspects at various levels of granularity, allowing the extraction of both fine-grained (local topics) and coarse-grained (global topics) aspects along with their associated textual evidence within reviews. [PERSON] and [PERSON] [29] utilized LDA for ATE from restaurant customer reviews, augmenting their work with a knowledge lexicon to improve the identification of named entities within the reviews. [PERSON] et al. [30] proposed a novel framework for named entity recognition (NER) in news articles, employing LDA to identify latent topics within the text and integrating a knowledge lexicon derived from Wikipedia to enhance entity extraction. This approach was tested on news datasets from the _New York Times_ and _Tech Crunch_, demonstrating its efficacy in extracting news-related entities. [PERSON] and [PERSON] [31] introduced a novel method to improve the quality of automatic ATE by integrating domain knowledge through word co-occurrence relationships. This method, known as Enriched Latent Dirichlet Allocation (ELDA), seeks to capture more nuanced aspects by incorporating co-occurrence information into the standard LDA model. [PERSON] et al. [32] proposed an ATE approach for analyzing hotel reviews in Mandalika, Indonesia, utilizing LDA. [PERSON] and [PERSON] [33] propose an innovative approach for ATE focusing on Turkish restaurant reviews. Their method utilizes Sentence Segment Latent Dirichlet Allocation (SS-LDA), an extension of the traditional LDA model, to tackle the challenge of limited data availability in the ATE task. [PERSON] et al. [34] tackle the limitations of LDA in handling overlapping topics by integrating BERT. This approach harnesses BERT's ability to capture semantic relationships between words, enhancing ATE performance by discerning between thematically similar aspects. 4. Hybrid ATE Methods: The last category of ATE methodologies comprises hybrid approaches.