## 1 Introduction  
Big data represents a revolutionary step forward from traditional data analysis, being characterized by three main components--variety, velocity and volume [1]. Moreover, as an emerging field of research and a practical method, big data has started an extensive discussion over the past years. The technology of the Internet, which has advanced in a fast-growing manner, offers, nowadays, new data collection channels for different businesses and management research domains. A big data approach started to be a must-have implementation in terms of data retrieving, storing, loading, and analyzing in many organizations, especially for the human resource area, which impacts or contributes directly to the performance of the company [2]. In today's context, even if good data can lead to better decisions, the high volume of the data collected from multiple external and internal sources can exceed the capacity of storage and analysis tools. Thus, to exploit the advantages deriving from Big Data analysis and from the use of Big Data analytics, there is a need to understand how data can be managed to improve decisions. Furthermore, recent studies have noted that there is still a sizable number of companies that fail to capture value from their big data investments [3], and even some that argue that big data may hurt rather than help companies [4]. As a result, there is insufficient understanding about how organizations should approach their big data initiatives, and scarce empirical support to guide value creation from such investments [5]. Moreover, in the past, it has been observed that human resources is a domain that differentiates a lot from other areas where artificial intelligence has been applied. One of the challenges is represented by the complicity of human resources outcomes. A second problem for data science is that many of the important outcomes in HR are relatively rare events, especially in small organizations. Additionally, since data science and machine learning require a large amount of data for analysis, the results obtained from applying different techniques can be poor [6]. Recently, in many artificial intelligence areas, text categorization has also gained importance, depending also on the size of text data and information. Due to the enormous potential of sentiment analysis for smart education and data-driven decisions, this technique has been deemed as a powerful analysis for identifying and classifying sentiments for multisource and multimodal data. This research paper aims to propose an end-to-end solution for the companies that would like to understand and make data-driven decisions, based on employees' feedback. The research will not only focus on how the data should be retrieved in an optimal approach, but also how it should be stored and analyzed, so that it can finally produce conclusions that can be used by the business units. To meet the purpose of this paper, the research methods consisted of data gathering through web scraping from various specialized websites, data cleaning and structuration to align data types and prepare the datasets for further analysis, data enrichments, aiming to label the observations and prepare the datasets for supervised machine learning algorithms, data analysis for a better understanding of the content, and analysis of the algorithms' results for an assessment of the best performing one for classification problems in the sentiment analysis area. For the later part, four classification algorithms were considered and applied on the two datasets obtained: Naive Bayes, Logistic Regression, K-Nearest Neighbor and Support Vector Machine. As a first step, which concerns the collection of data, according to a paper published by [PERSON] and [PERSON], scraped websites represent a big data source that generates a high volume of data [7]. Moreover, web scraping represents a technique used to extract data from different websites and transform it from an unstructured format to a structured standardized type by simulating human behavior.