## 4 Discussion and Conclusions
Therefore, we agreed that, based on this, we can continue to use this process to label all our data, even though we also agree that this performance can and should be improved. Therefore, the lexicon-based approach helped us add this variable with positive or negative sentiment for all the reviews and, in this way, we managed to overcome the challenges of not having a pre-labeled variable for sentiments that we needed for the following part. In addition, future research in the area might take into consideration the most recent challenges regarding the limited coverage of sentiment words for several domains [40; 41]. Moreover, to improve this kind of analysis, we can include in our future work self-learning method, which becomes more popular in this field, since these types of unstructured data are very difficult to find with labels associated. Recent research associated with this domain has been developed just in the past years; therefore, we can also try to extend our study using this technique. Moreover, we can try to understand even more on similar research regarding methods to improve the lexicon-based method's performance, apart from the data pre-processing and preparation, which has already been followed. Lastly, the next step in our process, respecting the previously presented methodology, was to continue with the newly created data as pre-labeled data (assuming that this new dataset was our base dataset) and apply different supervised machine learning techniques on them, such as Naive Bayes, KNN, Logistic Regression and SVM. At this moment, trying on the new dataset, different algorithms also provided us the chance to compare them and observe that, in our scenario, Logistic Regressions and SVM provided the best results. However, the run time of the SVM was almost four times higher than the one for Logistic Regression. Hence, if computational resources are also a selection criterion, Logistic Regression, even though not a benchmark among supervised models in text mining research, would be a better recommendation considering the results achieved, comparable with the ones obtained for SVM, but at a fraction of the cost. Additionally, considering that the performance was higher in three out of the four analyzed cases for the second dataset, which had more observations, we can conclude that machine learning classification algorithms perform better when they are trained on a higher volume of data. Moreover, we also wanted to explore the possibility of improving the performance of these four algorithms; therefore, we have also implemented hyperparameter tuning so that we can understand if better results can be obtained for our models. After this step where we introduced two different methods for this technique, we observed that we managed to obtain slightly better performances, with Logistic Regression being again the best algorithm in terms of performance. Finally, SVM did not manage to provide any results in around six hours of running it with the same resources that the other algorithms managed to use to provide outputs in less than an hour. Therefore, we concluded that, for high amounts of data and also for the current scenario, this algorithm should not be considered anymore. Additionally, to improve current research and results, other authors have proposed the parallelization and distribution of text classification. Moreover, other research in this field has also shown the fact that combining more classifiers for the analysis can improve classification accuracy [42]. Finally, we managed to obtain, as initially described, an end-to-end solution for our business need that can clearly determine an initial step for every organization that wants to take advantage of social platform data and use them in the context of big data analytics, without being dependent on other data collection and platforms available or different teams to initiate the analysis.