# A Domain-Independent Classification Model for Sentiment Analysis Using Neural Models
## 6 Discussion  
Based on our results, we could demonstrate the following points:  
1. Summarizing the final opinion in some words might lead to the problem that the extracted features did not take that sentence as a feature of interest. For example, a reviewer might have a  
Figure 4: The 10-fold cross-validation results for the trained SNN model using the IMDb model, which has been used on the MR and Amazon datasets.  
Figure 3: The cross-validation results for the trained CNN model using IMDb model, which has been used on MR and Amazon datasets.  
positive opinion about acting and the overall story of a movie, but he was not satisfied by the music in certain scenes.
* Sentiments can be expressed in different forms that might be even indirect expressions. Therefore, they require common sense reasoning techniques to be classified. In addition, it is challenging to analyze sentiments with complex structures of sentences, especially when negations do exist.
* Some reviewers may use expressions that have negative connotations, but at the end of the review, they summarize their overall opinion clearly. Consequently, this makes the classification a tough task.
* Feature engineering suffers from overcoming the previous problems.
* Sentiment analysis requires analyzing large units of individual words to capture the context in which those words appear.
* Table 8 shows that SNN shows a better generalization performance using the MR dataset compared to CNN. The results of other approaches are listed in Tables 9 and 10 for movie review data and IMDb, respectively. Some of the proposed approaches perform better; however, they do not consider the domain-independent sentiment classification. It means that the results are obtained for training and testing on the same dataset.  
We can observe that this work is:  
1. [leftmargin=*]
2. Able to classify binary reviews very well, especially, domain-independent reviews.
3. The first building block toward the generalization of sentiment classification where a model can be trained once and tested on totally new datasets that even may come from different contexts.
4. It inherits the advantages of neural models which is nowadays able to classify hundreds of objects using a pre-trained model.  
This is due to the fact that CNN can overcome many challenges of sentiment analysis that have been highlighted previously. For example, words in a specific region are more likely to be related than words far away. Thus, CNN can automatically and adaptively extract spatial hierarchies of features out of written reviews that may capture different writing styles of users.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Classifier Used** & **Accuracy** \\ \hline
[16] & SVM & 0.82 \\ \hline
[17] & Markov Model & 0.80 \\ \hline
[14] & Random Forest & 0.88 \\ \hline
[18] & statistical approach & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 10: A summary of the state-of-the-art precision metrics for IMDb, where SVM: Support Vector Machine.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paper** & **Classifier Used** & **Accuracy** \\ \hline
[19] & CNN & 0.81 \\ \hline
[20] & Autoencoders & 0.77 \\ \hline
[35] & SVM & 0.77 \\ \hline
[36] & DCNN & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 9: A summary of state-of-the-art performance metrics for MR, where DCNN: Dynamic Convolutional Neural Network, SVM: Support Vector Machine.