## 3 Methods and Results
### Data Modeling  
#### 3.4.1 Lexicon-Based Approach for Labeling Initial Set of Data  
After data were cleaned, we were having clearer words that were relevant for each review provided on the platforms mentioned before, but not the sentiment associated with each review. Therefore, based on classical methods that can provide us with a good text mining analysis, we can conclude that the current dataset is not appropriate for supervised machine learning techniques, since we do not have previous history on the review sentiment to learn from. Moreover, at this moment in our research, there is no need to consider splitting the dataset for any further processing of the data, since we will not be able to compare results with previous sentiment associated with the text. Considering the other approaches for our scenario, it is clear that a lexicon-based approach will help us improve the original dataset with a sentiment for each review provided. For our case, we have chosen TextBlob, which is a Python library used for natural language processing, to achieve these tasks. This library also gives easy access to different  
Figure 7: Overview of the final dataset. lexical resources and allows the user to work with classification, categorization, and some other tasks. For lexicon-based approaches, the sentiment will be defined by its semantic orientation and by the intensity of each work in the sentence. This also requests a predefined dictionary classifying negative and positive works. In general, a text will be represented by a bag of words and, after the assignment of individual scores to all the words, a final sentiment is calculated [17]. TextBlob will return the polarity and the subjectivity of a sentence. Polarity, which lies between \(-1\) and \(1\), can mean at \(-1\) a negative sentiment, and at \(1\) a positive one. Negation works reverse the polarity. Subjectivity lies between [0, 1]. Subjectivity quantifies the amount of individual opinion and factual information contained in the text. The higher subjectivity means that the text contains personal opinion rather than factual information. TextBlob has one more parameter--intensity. TextBlob calculates subjectivity by looking at the 'intensity'. Intensity determines if a word modifies the next word. For English, adverbs are used as modifiers ('very good') [30]. However, before we start with the application of the lexicon approach on our entire dataset, we would like to test this method's performance as well. Since we have previously expressed that, initially, this is not a pre-labelled dataset, we have decided that, in order to test its performance, we can manually label some of the inputs, apply the lexicon method and compare real sentiment with predicted sentiment. Therefore, we have taken a subset from the original dataset and added the associated sentiment. We have performed the same methodology expressed before in order to prepare the data, and afterwards we have applied TextBlob to predict the sentiments. Once this step was finished, we compared the predicted values with the real ones, and the results from this step can be seen in Table 4.  
We observe that the performance indicators obtained are very close to the ones retrieved from similar tests using the lexicon method, as it is mentioned in related research [31]. Moreover, in order to be able to feed to the next algorithms more data, we will use this to actually label all our records needed for training using the above approach, so that we can train our machine learning algorithms with more data than what we could manually label. Therefore, since we decided to continue with this method to label all our reviews, we applied again all the pre-processing steps on the initial dataset and used TextBlob for all the records. To be able to observe more visualizations on our data, we have imported the newly created dataset into a business intelligence platform.