## 3 Methodology
### Preprocessing Module
## 7 Exploring Aspect Terms Misprediction

This section delves into the classification of mispredictions occurring during the aspect term extraction process, comparing the classifications with the labels provided in the test datasets. The misprediction of aspect terms is categorized into two main types in Table 8: \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Theoretical** & **Actual/Min** \\ \hline Slang and Abbreviation Processing & \(O(n)\) & 0.08 \\ Multiple-Word Aspect Terms Extraction & \(O(n)\) & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The time complexity of the proposed algorithms. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Case** & **Review Sentence** & **Labeled Aspect Term** & **Predicted Aspect Term** & **Misprediction** \\ \hline
1 & one of my favorite places in brooklyn. & Null & brooklyn & (1) \\
2 & I trust the people at go sushi, it never disappoints. & people & people, sushi & (1) \\
3 & I highly recommend Mioposto. & mioposto & Null & (1) \\
4 & the best Chuwam Mushi I have ever had. & chuwam mushi & chawan mushi & (1) \\
5 & the menu is interesting and quite reasonably priced. & menu, priced & menu & (2) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Examples of aspect terms misprediction during the ATE Process. 1. False Positives and False Negatives: Instances where the model incorrectly predicts the presence or absence of an aspect term. 2. POS Tag Errors: Instances where the POS tagging of aspect terms leads to mispredictions. ### False Positives and False Negatives

False Positives and False Negatives consist of the following cases:

Case 1-2: The label fails to identify the aspect term while the model correctly predicts it as \"brooklyn\" or \"people and sushi\", indicating a False Negative. Case 3: The label designates \"mioposto\" as the aspect term, yet the proposed model fails to make the prediction. This failure stems from the absence of \"mioposto\" as a recognized entity in both the LOD and the Aspect Term corpus. As \"mioposto\" is a specific pizza restaurant name. Consequently, the proposed model lacks the necessary information to classify \"mioposto\" accurately, resulting in another instance of False Negative misprediction. Case 4: The ground truth label accurately identifies the dish as \"chuwam mushi\". However, the proposed model predicts \"chawan mushi\". This discrepancy likely arises from a word correction applied during the pre-processing module, resulting in a False Positive classification. ### POS Tag Errors

POS Tag Errors consist of the following case: Case 5: The model fails to recognize \"priced\" as an aspect term due to incorrect POS tagging, resulting in a misprediction categorized as a POS tag error. This section provides an analysis of various misprediction scenarios encountered during the aspect terms extraction process. Cases 1-4 illustrate instances of False Positives and False Negatives, while case 5 exemplifies a POS tag error. These findings underscore the intricate nature of aspect term classification and shed light on the limitations of SemEval datasets. By identifying aspect term mispredictions, this study contributes to enhancing the accuracy and reliability of aspect term classification models in natural language processing tasks. ## 8 Conclusions and Future Work

This study introduces a knowledge-driven approach aimed at automating semantic aspect-term extraction by utilizing the semantic capabilities of DBpedia. By leveraging DBpedia, our model seeks to enhance and enrich aspect extraction outcomes within the training dataset. Moreover, due to the extensive knowledge base of DBpedia, the proposed approach can be utilized for the identification of named entity aspect terms in other domains, including individuals, places, music albums, films, video games, organizations, species, and diseases [64]. To evaluate the efficacy of the proposed model, the research conducted experiments across three distinct datasets: SemEval 2014, 2015, and 2016.