## 4 Discussion and Conclusions  
As we have seen until now, this research highlights an automatic way to retrieve data and then applies different methods of analysis to provide insights for business audiences. In addition, it is critical to mention that this approach is not using pre-labeled data for the end-to-end process (and the lack of pre-labeled data is a well-known issue for current supervised algorithms in text and image processing), apart from one validation step that was possible due to the manual labeling for a subset of our input data. However, our main research is trying to consolidate a labeled dataset and use a mix of techniques. Some other research has been conducted around this kind of implementation as also explained by [PERSON] et al. [39], also due to the fact that high amounts of public and veridic text data are very hard to be found with sentiment associated with the text. Moreover, even if the implementation is similar, we can observe in the current research a different combination of techniques. Now, based on the above details, we conclude that the first part has focused on choosing the right social media platform with business and job-related information, retrieving the data in the most optimal and fast way, and also transforming the unstructured data into structured data, and loading it into a place from where it can be easily used for further analysis. Considering that the initial retrieval of the data was in an unstructured format that would have represented a downside for the next analysis we wanted to perform, we also introduced some steps to transform and aggregate the data, so that they can be used as wished for further analysis. We have observed that another challenge for our research was to take data from all the concerned sites since not all of them provide the same structure of the data. We found an option to improve the web scraper in order to use parameters and retrieve data from different websites--with the mention that only common information such as title, feedback, and rating can be used in the analysis. Another problematic step is the process of retrieving the data constantly or in a real-time manner so that we can always have the most recent feedback from current or former employees. The process can be automated to have data in almost-real time by creating a schedule to run at a predefined time interval. For our study, the web scraping technique applied helped us to automatically retrieve data by creating an algorithm that can be used to extract data from different websites such as Indeed or Glassdoor for any company. Nonetheless, the current dataset can be also improved by adding data from multiple websites (not only two sources as we have in the current analysis) or from multiple companies. Another approach can be trying to create a combined dataset with information from multiple sources using the common elements, such as the title of the review, feedback, and information regarding the author, location and date. Moreover, for future work, we are thinking of improving our study and overcoming the challenge of obtaining real data by integrating the current algorithm into an orchestration tool. The second part of our research was mainly focused on using the data obtained and realizing sentiment analysis on it, so that we can understand the bigger picture of the opinions a company receives from current and former employees. However, considering the techniques existing in the area, we were forced for the moment to apply initially a lexicon-based approach, since we did not have a high amount of pre-labeled data withvariable sentiment added for each review that was provided, a technique that does not seem to have been used before with the same purpose. Nonetheless, we have also introduced a quick validation step for our initial method that verifies text block lexicon method performance. This step managed to conclude that our method has a performance similar to the other implementations based on the same approach.