# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 3 Deep Learning Methods for Aspect Level Sentiment Classification
### Data Modelling Procedure for Deep Learning-Based ALSC
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Deep Learning Method**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Input to the Method**} & \multicolumn{2}{c}{**Neural**} & \multirow{2}{*}{**Attention Used**} & \multirow{2}{*}{**Syntactical Information**} \\  & & & & **Method** & & **in the** & \\  & & & & & **Architecture** & & \\ \hline \multirow{4}{*}{DL22} & ATAE- & \multicolumn{4}{c}{Utilizes aspect embedding along with} & \multirow{4}{*}{S, A} & \multirow{4}{*}{GRU} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} \\  & GRU([PERSON] et al., 2019) & & Attention-based GRU for generating the sentence vector representation. & & & \\ \hline \multirow{4}{*}{DL23} & \multirow{4}{*}{AT-BiGRU [7]} & Utilizes Bi-directional & \multirow{4}{*}{S} & \multirow{4}{*}{BiGRU} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} \\  & & GRU along with & & & & \\ \cline{1-1}  & AT-BiGRU [7] & attention mechanism to & & & & \\ \cline{1-1}  & GRU([PERSON] et al., 2019) & Attention Based GRU is & & & & \\ \cline{1-1}  & & used for generating the hidden vectors for the sentence. & & & & \\ \hline \multirow{4}{*}{DL24} & \multirow{4}{*}{AT-GRU [7]} & Utilizes Bi-direction & \multirow{4}{*}{S} & \multirow{4}{*}{BiLSTM} & \multirow{4}{*}{Yes} & \multirow{2}{*}{No} \\  & AT-BiLSTM [7] & attention mechanism to & & & & \\ \cline{1-1}  & & generate the hidden vectors for feeding into & & & & \\ \cline{1-1}  & & softmax function. & & & & \\ \hline \multirow{4}{*}{DL25} & \multirow{4}{*}{BiGRU [7]} & Bi-directional Gated & \multirow{4}{*}{S} & \multirow{4}{*}{BiGRU} & \multirow{2}{*}{No} & \multirow{2}{*}{No} \\  & & Recurrent Unit Network & & & & \\ \cline{1-1}  & & is used to generate the & & & & \\ \cline{1-1}  & & hidden vector that can be & & & & \\ \cline{1-1}  & & fed into the & & & & \\ \cline{1-1}  & & softmax layer. & & & & \\ \hline \multirow{4}{*}{DL27} & \multirow{4}{*}{BiLSTM [7]} & Bi-directional LSTM is & \multirow{4}{*}{S} & \multirow{4}{*}{BiLSTM} & \multirow{4}{*}{No} & \multirow{2}{*}{No} \\  & & used to generate the & & & & \\ \cline{1-1}  & & hidden vector that can be & & & & \\ \cline{1-1}  & & fed into softmax layer for & & & & \\ \hline \multirow{4}{*}{DL28} & \multirow{4}{*}{CNN [7]} & Uses simple Convolution & \multirow{4}{*}{S} & \multirow{4}{*}{CNN} & \multirow{2}{*}{No} & \multirow{2}{*}{No} \\  & & Neural Network for & & & & \\ \cline{1-1}  & & aspect sentiment polarity & & & & \\ \cline{1-1}  & & prediction. & & & & \\ \hline \multirow{4}{*}{DL29} & \multirow{4}{*}{GRU [7]} & Utilizes simple Gated & \multirow{4}{*}{S} & \multirow{4}{*}{GRU} & \multirow{2}{*}{No} & \multirow{2}{*}{No} \\  & & Recurrent Unit Network & & & & \\ \cline{1-1}  & & for generating hidden & & & & \\ \cline{1-1}  & & vector for a sentence & & & & \\ \cline{1-1}  & & which is fed into the & & & & \\ \cline{1-1}  & & softmax layer for & & & & \\ \cline{1-1}  & & prediction. & & & & \\ \hline \multirow{4}{*}{DL30} & \multirow{4}{*}{AEN-BERT [39]} & Attention Encoder & \multirow{4}{*}{S} & \multirow{4}{*}{GRU} & \multirow{2}{*}{No} & \multirow{2}{*}{No} \\  & & Network uses & & & & \\ \cline{1-1}  & attention-based encoder & & & & & \\ \cline{1-1}  & & network with pre-trained & & & & \\ \cline{1-1}  & & BERT embeddings for & & & & \\ \cline{1-1}  & & context target & & & & \\ \cline{1-1}  & & representation. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
**Dependency Graph**\(\rm D_{G}\) is an undirected graph similar to a dependency tree showing the relationship between different words of a sentence. **POS Tag**\(\rm POS_{Tag}\) represents the category of different words in the sentence. For, e.g., in the above sentence, the pos tags of various words are: { {'but': conjunction, 'the': Determiner, 'wine': Noun, 'list': Noun, 'is': Auxiliary verb, 'excellent': Adjective }. **Dependency Relation**\(\rm D_{R}\) is the relationship between two words in a sentence based on the dependency tree. The dependency relation is also known as typed dependency and dependency label.