## 2 Related Work
### Deep Neural Network Approaches at Document, Sentence, and Aspect Level  
In this subsection, we review the latest research efforts, presented in Table 1, with respect to the application of NNs for NLP tasks, focusing on document-, sentence-, and aspect-level analysis. We would like to clarify at this point that we split Table 1 into three subsections to make a clear distinction among the works conducted with respect to the three aforementioned levels of analysis. \begin{table}
\begin{tabular}{l l l l l l} \hline
**Work** & **Level of Analysis** & **Model** & **Dataset** & **Task** & **Evaluation Metrics** \\ \hline [PERSON] et al. [32] & Docs. & LSTM & Large Movie Review (50,000 reviews) & classification & Acc.: 80\% \\ \hline \multirow{3}{*}{[PERSON] et al. [33]} & \multirow{3}{*}{Docs.} & \multirow{3}{*}{RCNN} & 20 Newsgroups; & \multirow{3}{*}{classification} & Macro-F1: 96.49\% \\  & & & Fudan Set; & & Acc.: 95.2\% \\  & & & ACL Anthology Network; & & Acc.: 49.19\% \\  & & & SST & & Acc.: 47.21\% \\ \hline [PERSON] et al. [34] & Docs. & CNN + BLSTM & Large Movie Review (50,000 reviews) & classification & Acc.: 89.7\% \\ \hline [PERSON] and [PERSON] [35] & Docs. & CNN + LSTM & Large Movie Review (50,000 reviews) & classification & Acc.: 89.5\% \\ \hline \multirow{3}{*}{[PERSON] et al. [36]} & \multirow{3}{*}{Docs.} & \multirow{3}{*}{RNN} & SST (Sents.); & \multirow{3}{*}{classification} & Acc.: 49.6\% \\  & & & SST (Sents.); & & Acc.: 87.9\% \\  & & & Movie Reviews (subj/obj. reviews); & & Acc.: 94.1\% \\  & & & Large Movie Review (50,000 reviews) & & Acc.: 91.3\% \\ \hline \multirow{3}{*}{[PERSON] et al. [37]} & \multirow{3}{*}{Sents.} & \multirow{3}{*}{BiLSTM-CRF CNN} & MPQA opinion corpus; & \multirow{3}{*}{classification; target extraction} & - \\  & & & SST; & & Acc.: up to 88.3\% \\  & & & Movie Reviews (polarity v1.0) & & Acc.: 82.3\% \\ \hline \multirow{3}{*}{[PERSON] et al. [38]} & \multirow{3}{*}{Sents.} & \multirow{3}{*}{CNN + RNN} & SST; & \multirow{3}{*}{classification} & Acc.: 51.50\% \\  & & & SST; & & Acc.: 89.95\% \\  & & & Movie Reviews & & Acc.: 82.28\% \\ \hline [PERSON] et al. [39] & Sents. & Very Deep CNN & Product Reviews; News & classification & not reported \\ \hline \multirow{3}{*}{[PERSON] et al. [40]} & \multirow{3}{*}{Sents.} & \multirow{3}{*}{CNN + LSTM} & SST; & \multirow{3}{*}{dimensional regression} & RMSE/MAE/r: \\  & & & Chinese VA Texts & & 1.341/0.987/0.778 \\  & & & Chinese VA Texts & & RMSE/MAE/r: \\  & & & & 0.874/0.689/0.557 \\ \hline [PERSON] et al. [41] & Docs. & Deep CNN & Amazon reviews & aspect classification & Acc.: 94.38\% \\ \hline [PERSON] et al. [42] & Sents. & attention based LSTM & SemEval 2014 (Task 4) & aspect (binary) & Acc.: 89.9\% \\  & & & & classification & \\ \hline [PERSON] et al. [43] & Sents. & Deep CNN & SemEval 2014; & aspect extraction; & Acc.: up to 87.2\% \\  & & & Aspect-based dataset & classification & Acc.: 80.95\% \\ \hline [PERSON] et al. [44] & Sents. & Deep Memory Network & SemEval 2014 & aspect classification & Acc.: 80.95\% \\ \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of some of the latest published deep neural network models for sentiment analysis (SA) tasks. The table reports the level of analysis, the type of the deep neural network that has been applied (model), the datasets used, the type of task, and the evaluation metrics. The latter are labeled as follows: accuracy (Acc. ), Macro-F1 measure, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Pearson correlation coefficient (r). [PERSON] et al. [32] introduced a variation of long short-term memory (LSTM) and tested it on the Large Movie Review Dataset of 50K **documents**. It showed that even though accuracy was not increased compared to normal LSTM models, the stability and consistency were improved.