## 5 Conclusions and Future Work  
The fundamental hypothesis behind our model is that any user review encodes preferences, emotions and tastes that may be captured by latent factors that are common to similar users. These emotions are then open to be predicted as opinion keywords containing a sentiment strength score of item characteristics. In our model, every product has its own vocabulary, which is rich enough as to allow users to be expressive, but not too large as to increase the data sparsity--which burdens the learning process. A careful choice of opinion keywords for the domains under analysis proves to be crucial if we want to avoid topics of low descriptive value. We want to highlight that this article is, to the best of our knowledge, the first study that tries to predict the user's textual opinion by means of predicting the sentiment strength scores for item characteristics. The obtained results prove that the proposed model is indeed able to learn despite they still are far away to be considered in real environments. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**Opinion**} & \multicolumn{3}{c}{**Sentiment Strength Score**} \\ \hline
**Opinions in the Review** & **Topic** & **Sentiment Test** & **Unprocessed** & **Normalized** & **Predicted** \\ \hline
1. “There isn’t much to get excited about in a guitar stand,” & - & - & - & - & - \\
2. “however, it does its job and the price was right- & - & “price” & “right” & 2.000 & 0.250 & 0.272 \\
3. ’1 purchased four and they were all delivered on time.” & - & - & - & - & - \\
4. “Each adjusted to, and held, its guitar securely.” & “guitar” & “securely” & 2.000 & 0.500 & 0.403 \\
5. “There found the stand to be very stable.” & “stand” & “very, stable” & 4.000 & 0.500 & 0.123 \\
6. “The wells seem secure and the market beingven enough to do the job.” & “weld” & “securé” & 2.000 & - & - \\
7. “My music teacher has a similar stand which cost him 4x as much.” & - & - & - & - & - \\
8. “It does not appear to be of better quality.” & “quality” & “better” & 3.000 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A detailed example of the results obtained showing the original unprocessed review and the predicted sentiment scores for each opinion keyword in the product dictionary. Amazon dataset: ‘Musical Instruments’, product ID: B0018 TIADQ, user ID: A15 BHBF010 HV1F. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & \(\mathbf{F_{1}}\)**-Score** & **Accuracy** & **Precision** & **Recall** & **AUC** \\ \hline Musical Instruments & 0.77(2) & 0.64(3) & 0.87(1) & 0.68(4) & 0.51(1) \\ Automotive & 0.667(8) & 0.544(8) & 0.826(9) & 0.56(1) & 0.516(9) \\ Instant Video & 0.730(4) & 0.615(4) & 0.814(7) & 0.663(7) & 0.551(4) \\ Digital Music & 0.728(8) & 0.611(8) & 0.790(2) & 0.67(1) & 0.54(1) \\ Combined datasets & 0.726(5) & 0.609(5) & 0.801(2) & 0.664(9) & 0.537(3) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Model performance for several Amazon datasets (\(K=20\), \(\lambda=0.2\), \(n_{\text{iter}}=10\)). Values enclosed in parenthesis show the standard deviation at the least significant figure. Nonetheless, the model is still open to improvements and we expect it to be useful as a baseline for comparative purposes in SNS solutions. Our future work includes using larger datasets to verify the generalization capabilities of the system, since our experiments show a consistent reduction of MSE values with their size at least in domain dependent scenarios, as well as reduce the sparsity of the dataset by using the current prediction system (which has an accuracy above 60%) to generate new samples and populate incrementally a new dataset following a best-candidate sampling. We expect that this reduction of sparsity will improve the generalization capabilities of the system.