DATA ORGANIZATION MODELS AND THEIR OPTIMIZATION FOR SEMANTIC ANALYSIS OF SOCIAL MEDIA CONTENT
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Abstract
Introduction. This paper investigates fundamental approaches to data organization in
information systems and their application for optimizing semantic analysis of social media content.
Classical data models – hierarchical, network, and relational – are analyzed, and their advantages and
limitations in the context of processing large volumes of textual information are identified. The
methodology of content analysis as a tool for studying social communications and semantic text
analysis as a stage of automatic natural language understanding are examined. The relationship
between the choice of data organization model and the efficiency of semantic processing algorithms
for social media content is established.
The exponential growth of digital information, particularly in social networks, creates both new
opportunities and challenges for data analysis. Effective processing of such data arrays requires not
only powerful computing resources but also optimal data organization. The choice of data organization
model directly affects the speed of information access, the complexity of processing algorithms, and
the quality of final analysis results. Meanwhile, there is a growing need for automated content analysis
systems capable of identifying hidden patterns, trends, and semantic connections in large arrays of
textual data.
Content analysis as a research method originated in sociology and journalism but has
transformed into a powerful tool for automatic text processing with the development of computing
technology. Semantic analysis, which is a component of content analysis, allows identifying not only
explicit but also hidden content of messages, which is especially important for understanding public
sentiments, identifying trends, and predicting user behavior in social networks.
Purpose. The aim of this article is to analyze classical data organization models and determine
their optimality for the tasks of semantic analysis of social network content.
Results. The hierarchical model represents data in a tree-like structure with strict subordination
relationships but has limited flexibility for representing complex many-to-many connections. The
network model extends the hierarchical approach by allowing multiple parent relationships but
increases system complexity. The relational model, based on set theory and relational algebra,
provides data independence and declarative query language but may have performance limitations for
certain operations.
Modern data models, including document-oriented and graph databases, better meet the
requirements of social media content processing due to schema flexibility and optimization for
distributed systems. Hybrid architectures combining different types of storage depending on data
nature and operations are identified as the most promising direction. For effective semantic analysis,
the organization of indexes that provide fast search by both lexical and semantic features is critically
important.
Conclusion. The paper examines content analysis stages including source selection, sampling,
unit of analysis identification, and results interpretation. Semantic analysis methods are reviewed,
including sentiment analysis, entity recognition, and topic modeling. The relationship between data
organization model choice and semantic analysis algorithm efficiency is established.
Recommendations for optimizing data structures for processing large volumes of social media content
are provided.
The research demonstrates that no single data model is universally optimal for all aspects of
social media content analysis. Document-oriented databases are suitable for storing raw heterogeneous
data, relational databases for storing structured analysis results, and graph databases for representing
social connections. Distributed processing and stream processing technologies are essential for realtime
analysis of large-scale social media data.
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