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1. 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 semi-
structured 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).

2. 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.

3. 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.

4. 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.

5. 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.
References