## 3 Experiments
### Experimental Design  
We have performed five experiments related with each one of the datasets (musical instruments, automotive, instant video, and digital music) to test the learning capabilities of the proposed method. Moreover, we performed an additional experiment to test the generalization capabilities by joining the fours datasets to create a bigger one that is domain independent. This implies that the vocabulary is global, and the goal is to test how large is the difference between domain dependent and independent approaches. We evaluated our model performance and set the ALS parameters \((K,\lambda,n_{\text{iter}})\) for each dataset by means of a 5-fold cross-validation. The performance was measured by analyzing the Mean Squared Error (MSE), accuracy, \(F_{1}\)-score, precision, recall and Area Under the Curve (AUC) metrics. The MSE measures the error obtained by the prediction model during the cross-validated learning process and has been used to prevent overfitting. The accuracy provides an overall success criterion \((\frac{TruePositive+TrueNegative}{Totalpopulation})\) without considering the data distribution. The precision \((\frac{TruePositive}{predictedPositive})\) provides the percentage of the correctly predicted opinion keywords and their sentiment. Recall metric \((\frac{TruePositive}{ConditionPositive})\) provides the percentage of the correctly detected opinion keywords and their sentiment, and the \(F_{1}\)-score \((2\frac{Precision+Recall}{Precision+Recall})\) has been used as application metric success because it is equally important to detect an opinion keyword that exists than not to say that exists when is not. Finally, the AUC metric has been used to verify that the obtained model behavior is independent of the data distribution. \begin{table}
\begin{tabular}{l l} \hline
**Text review ID** & **Extracted opinion dictionaries** \\ \hline B00 CCCOBOI4 (Automotive) & 3,month, car, instruction, job, layer, paint, problem, product, a result, stuff, surface, thing \\ \hline B002 CCZUUG (Musical Instruments) & action, finish, guitar, neck, pickup, sound, review, string, quality, way \\ \hline B003 VWJ2K8 (Musical Instruments) & battery, buy, clip, deal, display, design, color, guitar, head, item, job, price, problem, product, purchase, quality, result, Snark, Snark, Snark, SN-1, spot, string, thing, time, tune, tuner, turing, use, value, work \\ \hline \end{tabular}
\end{table} 
Table 4: Some items’ dictionaries. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Reviews** & **Users** & **Products** & **Number of Entries** & **Ratio of Positive vs. Negative/neutral** & **Sparsity** \\ \hline Musical Instruments & 10,261 & 1429 & 900 & 41,942/6158 & 87.1\% & 97.5\% \\ Automotive & 20,473 & 2929 & 1835 & 75,917/9835 & 82.0\% & 98.9\% \\ Instant Video & 37,126 & 5129 & 1685 & 165,899/34,802 & 78.8\% & 97.8\% \\ Digital Music & 64,706 & 5536 & 3568 & 483,232/91,801 & 77.1\% & 99.88\% \\ Combined datasets & 132,566 & 14,809 & 7988 & 767,080/142,689 & 78.3\% & 99.95\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Amazon dataset features. To obtain the input matrix \(S\) we first processed the reviews using NLP and the opinion dictionary generation method explained in Section 2.1 and then obtained the optimal parameters by conducting the following experiments:  
* Combined analysis of \(\lambda\in\{0.01,0.02,0.03,0.04,0.05,0.07,0.1,0.2,0.3,0.4,0.5,0.7\}\) and \(K\in\{5,10,20,50,100,200\}\) parameters to probe whether they can be set independently for each dataset. * Analysis of the influence of the optimal \(K\) value on the model performance for different datasets and \(\lambda\) values. * Analysis of the number of iterations needed until convergence is reached. We want to point out that despite the use of a cluster, it was still very time-consuming to perform an exhaustive trial and error for all the possible parameter combinations of the model.