Graduation Thesis Sample

Big Data and Semantic Analysis In this thesis several relatively new concepts will be mentioned that have already begun changing the scope of today's business. These are the Big Data and semantic analysis concepts. In these thesis, the potential of this topic was explored, and this work expanded these two terms and linked them to concrete applications in business organizations. Although authors and researchers still cannot agree on what is the specific definition of the term Big Data, often in the literature, in an effort to describe the complexity of this term, the so-called V's approach is mentioned. Most authors, like those who will be quoted in this paper, use 4 V: Volume, Variety, Velocity and Veracity.

Big Data solutions are ideal for analysis of not only structured data, which business organizations are used to analyze, but also unstructured and semistructured data that often come from different sources. In this paper, special attention will be paid to unstructured data. Specifically, textual data from social networks and popular websites will be researched. Large data is considered to be ideal when it is necessary to analyze all data that is considered relevant for better understanding of clients. The other term referred to is semantic analysis. The goal of semantic analysis is to understand the meaning of a particular linguistic input. Therefore, the data is collected, the text is converted into a number, and the obtained results are used in further business analysis, which leads to an increase in the value of existing analyzes and outputs, since these data were unavailable to us (at least small and medium-sized enterprises).Â

Semantics deals with the analysis of meaning and stands at the center of a linguistic quest to understand the nature of language and linguistic abilities.

Sentiment analysis or analysis of thinking is a field of science that analyzes human thoughts, feelings, praise, attitudes and emotions towards different products, services, organizations, people, problems, events and their attributes. Therefore, in this paper, the semantic analysis will analyze the opinions of people published on social networks and websites. Both concepts (Big Data and Semantic Analysis) have been known and have existed for quite some time, but in recent years, with the development of Big Data technology, the price of this kind of analysis has been reduced, and the potential of unstructured data has been exploited. Particular attention should be paid to the economic viability of this type of unstructured data (textual data) in modern business. Most authors deal with the technological problem and the technological feasibility of this type of analysis, while the economic aspect is often unfairly ignored.

Research subject Today, companies are increasingly paying attention to the data-driven way of thinking and doing business. That is, their decisions are driven by data.

Data needs considerably increase; companies require more and better quality and more diversified data, with the aim of extending their analysis and gaining a wider view of their customers. The question arises: is it possible to get quality data that can contribute to decision-making in modern business? The contribution will be analyzed through a prism of technological and economic approach. The Big Data technologies that support this kind of analysis will be analyzed, as well as the models for semantic analysis and selection of optimal and usability for business decision making.Â

In order to analyze the state of this type of analysis, a concrete project financed by the European Union, which meets all the above criteria, will be used. The entire path required for semantic analysis will be processed. From collecting data, saving it, ETL, modeling, creating outputs, and utilizing outputs to make decisions. Frequently, the problem is that during the implementation, the moment of exploitation of the decision-making output is reached, with often delaying or deterioration of the project itself. Sources of this problem will also be identified in this paper.

Research hypotheses An important part of the paper is dedicated to setting up appropriate research hypotheses. Hypothesis (Greek hypothesis, assumption) is the acceptance of the assumption on which a conclusion is based, which serves for advancement of research and explanation, without being proven by other principles and not confirmed by (verified) experience. Therefore, the goal is to prove, or not to reject, hypotheses.

The following research hypotheses should be based on the applicative research, verify truthfulness. The hypotheses are: Ho: Semantic analysis of unstructured data supported by Big Data technology is usable for business decision making H1: Semantic analysis of unstructured data supported by Big Data technology is not usable for business decision-making To determine whether the semantic analysis of unstructured data is supported by Big Data is usable for business decision-making, it is necessary to determine whether the model obtained by semantic analysis has sufficient quality output, which can be used for business decision-making.Â

Therefore, in this paper we will examine these sub-hypotheses: Ho: Data obtained by semantic analysis of unstructured data supported by Big Data technology is of high quality.

H1: Data obtained by semantic analysis of unstructured data supported by Big Data technology is not of high quality. The quality of data obtained by semantic analysis of unstructured data supported by Big Data technology will be determined by: - The time it takes from the start of the analysis to the creation of the output based on analyzes, - The amount of resources needed for this type of analysis - Using the accuracy of the model.

Research aim The aim of the research is to confirm the hypotheses. By interpreting the hypotheses, the goals are reduced to the conclusion that the semantic analysis of unstructured data is supported by Big Data technology usable for business decision making. The achievement of objectives will be achieved by applying the methodological framework, which is explained in more detail in the next chapter. The backbone of the research will be the implementation of applied research on a concrete example of the project and evaluating the results obtained. The results obtained should be used in order to better understand the maturity of Big Data technology and semantic analysis of textual data for the delivery of quality data for business decision-making purposes on the example of this company's project.

References Aggarwal Charu C. & Zhai, C. X. (2012). Mining Text Data, USA: Springer.

Richert, W. & Coelho, L. P. (2013). Building Machine Learning Systems with Python, UK: Packt Publishing.

Harris, H. et al. (2013). Analyzing the Analyzers, an Introspective Survey of Data Scientists and Their Work, USA: O'Reilly.