# Adaptive Local Context and Syntactic Feature Modeling for Aspect-Based Sentiment Analysis
## 6 Conclusions and Future Work  
We propose an end-to-end ABSA solution, which reflects the importance of local context information in different tasks. Therefore, fine-grained information is essential in task learning. Our proposed adaptive semantic relative distance approach relies on text-dependent syntactic structures. A suitable local context is determined based on the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Baseline**} & **Domain** & \multicolumn{2}{c}{**Laptop**} & \multicolumn{2}{c}{**Restaurant**} \\ \cline{2-6}  & **Model** & **F1** & **Acc** & **F1** & **Acc** \\ \hline \multirow{6}{*}{LSTM} & TD-LSTM & 68.43 & 71.83 & 66.73 & 78 \\  & IAN & 70.81 & 72.5 & 67.38 & 72.05 \\  & RAM & 71.35 & 74.49 & 70.8 & 80.23 \\  & AOA & 67.52 & 72.62 & 70.42 & 79.97 \\  & MGAN & 72.47 & 75.39 & 71.94 & 81.25 \\ \hline GCN & ASGCN & 71.05 & 75.55 & 72.19 & 80.86 \\ \hline \multirow{6}{*}{BERT} & BERT-SPC & 75.55 & 79.4 & 80.52 & 86.77 \\  & AEN-BERT & 76.31 & 79.93 & 73.76 & 83.12 \\  & BERT-PT & 75.08 & 78.07 & 76.96 & 84.95 \\  & LCF-BERT-CDW & 76.2 & 80.21 & 79.12 & 85.91 \\  & LCF-BERT-CDM & 76.2 & 79.65 & 78.74 & 85.73 \\  & LCFS-ASC-CDW & 77.13 & 80.52 & 80.31 & 86.71 \\  & LCFS-ASC-CDM & 76.45 & 80.34 & 80.10 & 86.13 \\ \hline \multirow{2}{*}{Our} & Ours-model-ASC-M1 & **77.51** & **81.45** & 80.64 & 86.6 \\  & Ours-model-ASC-M2 & 77.3 & 80.35 & **81.21** & **86.86** \\ \hline \hline \end{tabular} Note: The best result in each dataset is highlighted in bold.  
\end{table}
Table 4: Comparison of our ASC model with state-of-the-art methods in terms of accuracy and F1 score (%).  
text length. In aspect term extraction tasks, equip-width convolutional neural networks can efficiently aggregate local information. Other convolutional neural networks will be explored later to apply them to this task. From the experimental results, A-ABSA uses adaptive semantic relative distance to improve the accuracy and F1 by 1.24%, 1.31%, and 0.95%, 2.09%, on the laptop dataset and restaurant data, respectively, compared to LCF-BERT. This validates the effectiveness of the adaptive relative distance method. Our proposed solution is applicable to long-text comment information and cannot effectively model contextual syntactic relationships for short-text information. Our proposed model does not make effective use of labeling information, using different classification models to solve different aspects of the sentiment analysis subproblem, and never being able to link the two more closely. The use of generative models to solve the ABSA multitasking problem has been proposed as a preliminary exploration. It would be worthwhile to investigate whether the combination of existing methodological theories with it would further promote the development of ABSA.  
Conceptualization, J.H., Y.C. and S.W.; Methodology, J.H., Y.C. and S.W.; Validation, J.H.; Formal analysis, J.H.; Investigation, J.H., Y.C. and S.W.; Data curation, J.H.; Writing--original draft, J.H.; Writing--review & editing, Y.C. and S.W.; Visualization, J.H.; Supervision, Y.C. and S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Innovation Team of Digital Agriculture Agricultural Technology System grant number BAIC10-2022-E10 and funded by Basic Scientific Research Fund of Chinese Academy of Agricultural Sciences grant number 2021 JKY029.  
Not applicable.  
The authors declare no conflict of interest.