## 3 Methodology
### Preprocessing Module
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Research Study** & **Methodology** & **Model/Methods** & \begin{tabular}{l} **SemEval 2014** \\ **(F1)** \\ \end{tabular} & \
\begin{tabular}{l} **SemEval 2015** \\ **(F1)** \\ \end{tabular} &
\begin{tabular}{l} **SemEval 2016** \\ **(F1)** \\ \end{tabular} \\ \hline [PERSON] et al. (2017) [12] & Supervised & MTNN+Bi-LSTM+CNN & 0.83 & 0.67 & 0.72 \\ [PERSON] et al. (2018) [13] & Supervised & MTNN+GI & 0.84 & 0.71 & - \\ [PERSON] et al. (2019) [14] & Supervised & SDFC & 0.84 & 0.71 & 0.74 \\ [PERSON] et al. (2019) [15] & Supervised & BiDTreeCRF & 0.85 & 0.71 & 0.74 \\ [PERSON] et al. (2020) [16] & Supervised & Bi-LSTM & 0.83 & - & - \\ [PERSON] et al. (2018) [35] & Hybrid & Rules+GRU & 0.76 & 0.63 & 0.64 \\ [PERSON] et al. (2020) [36] & Hybrid & Rules+Bi-LSTM & - & - & **0.79** \\ [PERSON] et al. (2019) [26] & Unsupervised & Syntax & - & 0.60 & 0.67 \\ [PERSON] et al. (2022) [34] & Unsupervised & LDA+BERT & 0.81 & 0.74 & 0.75 \\ [PERSON] et al. (2024) [41] & Unsupervised & BERT & 0.82 & 0.69 & - \\
**Proposed approach** & Unsupervised & LOD & 0.80 & **0.76** & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons with the existing state-of-the-art baselines. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Precision** & **Recall** & **F-Measure** \\ \hline SemEval 2014 & 0.82 & 0.80 & 0.80 \\ SemEval 2015 & 0.78 & 0.75 & 0.76 \\ SemEval 2016 & 0.79 & 0.76 & 0.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Result of the proposed model for ATE on SemEval datasets. et al. (2024) [41]. This suggests that the proposed model excels at accurately identifying aspect terms in the dataset SemEval 2015, demonstrating its robustness and effectiveness in this particular domain. Moving to the dataset SemEval 2016, our approach achieves an F-measure of 0.77, which is slightly lower than the maximum F-measure attained by the approach of [PERSON] et al. (2020) [36] (F-measure = 0.79). However, the proposed approach still performs competitively compared to other approaches, such as [PERSON] et al. (2017) [12], [PERSON] et al. (2019) [14], [PERSON] et al. (2019) [15], [PERSON] et al. (2018) [35], [PERSON] et al. (2019) [26], and [PERSON] et al. (2022) [34]. Overall, the experimental results highlight the effectiveness of the proposed model for aspect term extraction across different datasets, while the proposed approach may not always achieve the highest F-measure in every dataset, it consistently demonstrates competitive performance and, in some cases, outperforms other approaches. This indicates the versatility and reliability of the proposed model in capturing aspect terms across diverse contexts within the restaurant domain. Further refinements and optimizations could potentially enhance its performance even further in future experiments. ## 6 Time Complexity Analysis

The time complexity evaluation for the proposed algorithms is presented in Table 7. The actual running time has been measured using restaurant training datasets. It is assumed in this research that the maximum sentence length is derived from these training datasets, with the maximum sentence length being 61 words. Consequently, the total time complexity of the proposed algorithms is \(O(n)\)[35], indicating a linear time complexity, where \(n\) represents the maximum length of a sentence. According to Table 7, the proposed algorithms' time complexity reflects their efficiency in processing restaurant reviews. Slang and Abbreviation Processing and the Multiple-word Aspect Terms Extraction algorithm operate in theoretical & \(O(n)\) time complexity and achieve an actual processing time of 0.08 min and 0.05 min, respectively. The Slang and Abbreviation Processing algorithm's efficiency ensures rapid processing, even for longer sentences. The Multiple-Word Aspect Term Extraction algorithm's efficiency is critical for identifying nuanced aspects of restaurant reviews without undue computational overhead.