# LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification
## 4 Experiments  
### Datasets and Hyperparameters  
Experiments are conducted on three ABSC benchmark datasets, a review dataset of laptop, a review dataset of restaurants from SemEval-2014 [1] and a common ACL 14 twitter social dataset introduced by [32]. According to the experimental results, the performance of the LCF design on the datasets of three topics (restaurant reviews, laptop reviews, and tweets) has been significantly improved, which indicates that the LCF design does not rely on specific datasets or corpora and it is applicable to most topics of datasets, which is due to the original characteristics of natural language (Suppose people comment on multiple aspects in a review, then, the context word used to judge an aspect is likely to be near the aspect generally, and not far from the aspect word to be commented on.). These datasets are adopted by most of the proposed models and are the most popular datasets of ABSC task, and a large number of experiments have been carried out on these datasets for comparison. These datasets provide labeled aspects such as the sentiment polarities of the aspects. All aspects of the above datasets are labeled in three categories of sentiment polarities: positive, neutral, and negative. In the experiments, these datasets are original and under no refactor, and no conflicting label is removed. In that case, it can provide a better estimate of the real performance of the model. Table 2 demonstrates the details of three datasets (Generally, both LCF-GloVe and LCF-BERT converge within three epochs during the training process.).  
To evaluate the precise difference of performance, the hyperparameters are kept consistent for LCF-GloVe model and LCF-BERT model except for learning rate, because BERT-shared layer requires very small learning rate during fine-tuning process [15]. For LCF-GloVe, the learning rate is set to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**Positive**} & \multicolumn{2}{c}{**Negative**} & \multicolumn{2}{c}{**Neural**} \\ \cline{2-7}  & **Train** & **Test** & **Train** & **Test** & **Train** & **Test** \\ \hline Laptop & 994 & 341 & 870 & 128 & 464 & 169 \\ Restaurant & 2164 & 728 & 807 & 196 & 637 & 196 \\ Twitter & 1561 & 173 & 1560 & 173 & 3127 & 346 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detail of benchmark datasets.  
\(1\times 10^{-3}\). The hidden dimension and embedding dimension, \(d_{h}\), and \(d_{e}\), are set to 300. For LCF-BERT, the learning rate is set to \(2\times 10^{-5}\). The hidden dimension and embedding dimension are set to 768 in LCF-BERT. For both LCF design models, dropout rates are set to 0, \(L_{2}\) regularization is set to \(1\times 10^{-5}\), and the batch size is set to 16. In addition, LCF models utilize the Adam optimizer [33].  
All hyperparameters in this paper have been supported by a large number of comparative experiments. Among them, most of the hyperparameters follow the common hyperparameter settings of this task, such as the word embedding dimension and learning rate of LCF-GloVe model. We tried different thresholds to find the optimal threshold for each dataset (see Table 5). The fine-tuning process of BERT is very sensitive to the learning rate. Only a small learning rate can maximize [PERSON]'s performance, which has been illustrated in the original paper of BERT. Through the experiments, we observed that when batch size was large, the instability of regularization between layers would reduce the performance of the model, so the optimal batch size of 16 was adopted. Large dropout will prolong the convergence rate of the LCF-BERT model, and experimental results show that they have no obvious influence on LCF design. Therefore, after comprehensive consideration, dropout of the model is designed to be 0. For both LCF design models, accuracy and Macro-F1 score are adopted to evaluate the performance of LCF designs. Because experimental results tend to fluctuate, this paper chooses the best experimental results to make comparisons.