# JUMRv1: A Sentiment Analysis Dataset for Movie Recommendation
## 5 Results and Discussion
#### Analysis Metrics  
In order to analyse the performance of our model on various datasets, we considered the standard performance metrics, namely the F1 score and the accuracy score with their corresponding class support division. Precision is defined as:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
Recall is defined as:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
Accuracy score is defined as:  
\[\text{Accuracy-score}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{ FP}+\text{FN}} \tag{7}\]  
Here, TP (True Positive) = Number of reviews correctly classified into corresponding sentiment classes. FP (False Positive) = Number of reviews classified as belonging to a sentiment class that they do not belong to. FN (False Negative) = Number of reviews classified as not belonging to a sentiment class that they actually belong to. The F1 score is defined as:  
\[\text{F1-score}=2\times\frac{\text{Precision}\times\text{Recall}}{(\text{ Precision}+\text{Recall})} \tag{8}\]  
Support for a sentiment class is defined as the number of reviews that lies in that sentiment class. Figure 5 shows the F1 scores that we obtained using GloVe word embeddings with 200 features (i.e., no feature selection). Table 3 denotes the F1 scores received via Glove embeddings, but with the help of different feature selection methods, selecting 150, 100, and 50 of the most important features. Figure 5: F1 scores for GloVe embeddings, without any feature selection methods. Google Word2 Vec word embedding is one of the most popular embeddings used in NLP. Here, we used the Word2 Vec pre-trained model, which was trained on the Google News corpus with 100 billion words in its vocabulary. It has 300 features and using different feature selection methods, we ranked these features and selected the top 150, 100, and 50 features accordingly. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Selection** & **Features Selected** & **Classifier** & **F\({}_{1}\)-Score** \\ \hline Recursive (RF) & & RF & 0.6667 \\ Recursive (RF) & & XGB & 0.6667 \\ Recursive (RF) & & SVC & 0.6045 \\ Recursive (XGB) & & RF & 0.6384 \\ Recursive (XGB) & & XGB & 0.6384 \\ Recursive (XGB) & & SVC & 0.5593 \\ Chi-Squared & & RF & 0.6158 \\ Chi-Squared & 150 & XGB & 0.7006 \\ Chi-Squared & & SVC & 0.6441 \\ Mutual Info & & RF & 0.6441 \\ Mutual Info & & XGB & 0.661 \\ Mutual Info & & SVC & 0.5593 \\ F Classifier & & RF & 0.6441 \\ F Classifier & & XGB & 0.6667 \\ F Classifier & & SVC & 0.565 \\ \hline Recursive (RF) & & RF & 0.6553 \\ Recursive (RF) & & XGB & 0.6327 \\ Recursive (RF) & & SVC & 0.6158 \\ Recursive (XGB) & & RF & 0.644 \\ Recursive (XGB) & & SVC & 0.5593 \\ Chi-Squared & & RF & 0.644 \\ Chi-Squared & 100 & XGB & 0.6836 \\ Chi-Squared & & SVC & 0.6666 \\ Mutual Info & & RF & 0.6779 \\ Mutual Info & & XGB & 0.6836 \\ Mutual Info & & SVC & 0.5649 \\ F Classifier & & RF & 0.6497 \\ F Classifier & & XGB & 0.6892 \\ F Classifier & & SVC & 0.5649 \\ \hline Recursive (RF) & & RF & 0.644 \\ Recursive (RF) & & XGB & 0.6779 \\ Recursive (RF) & & SVC & 0.6214 \\ Recursive (XGB) & & RF & 0.649717 \\ Recursive (XGB) & & XGB & 0.649715 \\ Recursive (XGB) & & SVC & 0.6271 \\ Chi-Squared & & RF & 0.6779 \\ Chi-Squared & & SVC & 0.6666 \\ Mutual Info & & RF & 0.644 \\ Mutual Info & & XGB & 0.6836 \\ Mutual Info & & SVC & 0.5932 \\ F Classifier & & RF & 0.6892 \\ F Classifier & & XGB & 0.6553 \\ F Classifier & & SVC & 0.6327 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F\({}_{1}\) scores obtained by using GloVe pre-trained embeddings and selecting 150, 100, and 50 features. Figure 6 shows the F\({}_{1}\) scores for Google's pre-trained Word2 Vec embedding with all 300 features (i.e., no feature selection). Table 4 shows the F\({}_{1}\) scores obtained on the same embedding upon selection of the top 150, 100, and 50 features.