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SENTIMENT ANALYSIS RESEARCH AND IMPORTANCE
Suyunova Malika Adil kizi
Tashkent State University named after Alisher Navoi
University of Uzbek Language and Literature
Computational linguistics and digital technologies
PhD student of the department
Email:
Annotation:
This article examines the issues of sentiment analysis, which is a branch of
computational linguistics, and its study in the context of world linguistics. Computational
linguistics is a scientific field focused on understanding and processing human language with the
help of computers, and the importance of sentiment analysis is growing day by day. By detecting
and classifying emotional states in text, it is possible to analyze various practical areas, including
social networks, customer reviews, and other texts. The article provides detailed information on
the main methods and techniques of sentiment analysis, as well as approaches to its study in
linguistics. It emphasizes the importance of the semantic characteristics of language, contextual
analysis, and cross-lingual research.
Keywords:
sentiment analysis, language processing, emotional analysis, natural language
processing (NLP), cross-lingual research, semantics, linguistics, social networks, artificial
intelligence, grammatical analysis.
Аннотация:
Эта статья изучает вопросы компьютерной лингвистики, связанные с
анализом сентимента и его исследованием в контексте мировой лингвистики.
Компьютерная лингвистика — это научная область, направленная на понимание и
обработку человеческого языка с помощью компьютеров, и значение анализа сентимента
с каждым днем растет. Определение эмоционального состояния в тексте и его
классификация позволяют проводить анализ в различных практических областях, включая
социальные сети, отзывы клиентов и другие тексты. В статье представлены основные
методы и подходы анализа сентимента, а также подходы к его исследованию в
лингвистике. В частности, подчеркивается важность семантических характеристик языка,
контекстуального анализа и кросс-языковых исследований.
Ключевые слова:
анализ сентимента, обработка языка, эмоциональный анализ,
естественная обработка языка (NLP), кросс-языковые исследования, семантика,
лингвистика, социальные сети, искусственный интеллект, грамматический анализ.
Introduction.
Today, the demand for information technologies is increasing day by day. All
industries, enterprises and companies in the world have been covered by computer technologies.
As technologies develop, competition is also increasing by promoting products and services on
social networks. The increase or decrease in demand for a product depends on whether the
opinions given to it on social networks are positive or negative. Today, the flow of feedback on
products and services on social networks is increasing immensely. This requires a lot of
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manpower to determine whether each expressed opinion is positive, negative or neutral. The
human factor cannot analyze this level of large information in one day or several days. However,
the number of data and comments increases every hour. Solving these and similar language-
related issues using computer models is one of the current issues in the field of natural language
processing (NLP). Analyzing the feelings expressed by a person to a particular product, service
or brand is called sentiment analysis. This is one of the most active research areas in natural
language processing. Sentiment analysis is one of the important areas in the field of detecting
emotional states in text and computational linguistics. This method serves to identify positive,
negative or neutral emotions expressed in texts and on this basis, analyzes texts in various fields,
including customer reviews, social networks, advertising materials and other texts. The study of
sentiment analysis is also widespread in world linguistics, and research in this area helps to
understand not only analysis methods, but also the semantic, syntactic and contextual features of
language [1].
The main goal of sentiment analysis is to determine the emotional state of a text or word. For
example, a customer review of a product indicates a positive or negative attitude. Sentiment
analysis uses computational linguistics technologies to analyze the position, grammatical
structure, and meaning of words in the text [2].
Sentiment analysis falls into three main categories:
1.
Positive: The essence of the text or word conveys a good or positive feeling.
2.
Negative: The text or word expresses negativity or disapproval.
3.
Neutrality: The emotional content of a text or word is either unclear or emains neutral.
Various methods help perform this analysis:
Word-based analysis: This method determines the emotional image (positive or negative)
of each word.
Phrase and context-based analysis: In a full text analysis, the context and grammatical
structure between words are considered.
Machine learning technologies: Using artificial intelligence and deep learning techniques,
the accuracy and efficiency of sentiment analysis are increased.
Linguists analyze the semantic (meaning) aspects of words in different languages. For sentiment
analysis, it is important to understand the specific features of this language, because each
language has its own grammatical and lexical features. Since the semantic and lexical systems of
each language are different, translinguistic (cross-linguistic) studies of sentiment analysis are of
great importance in world linguistics. Linguists study how to identify positive and negative
words in several languages, and how these words change in cultural context. Sentiment analysis,
especially if there are differences between languages, can give erroneous results if performed
solely based on words. Therefore, in world linguistics, focus is placed on analyzing the text as a
whole, because the meaning of words depends on their context, structure, and cultural features.
Sentiment analysis studies not only the structure of the language, but also the author's intention,
his cultural and sociological context [3]. Linguists are also studying how accurate and reliable
sentiment analysis can be made of sentiments that emerge on social media or online platforms.
The study of sentiment analysis in the field of computational linguistics forms an important
bridge between linguistics and computer science. This field provides the opportunity to analyze
the natural structure of language using computers, to identify emotional states between texts, and
to enhance international interactions. For world linguistics, sentiment analysis helps to
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understand the emotional and contextual factors of language, creating opportunities for cross-
linguistic research, cultural and social analysis.
The significance of the study.
This study aims to provide an in-depth analysis of scientific
research and practical applications in the fields of computational linguistics and sentiment
analysis. Today, with the development of technology and artificial intelligence, the capabilities
of sentiment analysis are expanding, and its analytical and scientific aspects are increasing as
well. The processes of analyzing texts, identifying emotional states and classifying them using
computational linguistics are widely used in various fields, including marketing, social networks,
customer opinion research, and obtaining feedback on products. The importance of the study
consists in showing how sentiment analysis has bin studied in world linguistics and the role of
this analysis in determining the semantic and contextual features of language. Deepening the
interaction between linguistics and computer science and developing cross-language research
will serve the advancement of modern linguistics and language processing technologies. At the
same time, how sentiment analysis differs across different languages and cultures and its impact
on scientific and applied fields increases the relevance of the research.
Today, sentiment analysis of Uzbek texts is one of the most relevant issues. This, in turn, allows
using this data for various purposes, including marketing, politics, social research, and other
areas. Sentiment analysis allows you to determine not only the general emotional direction of the
text, but also the author's attitude to a particular object or topic.
Literature review.
A number of important studies have emerged in the fields of computational
linguistics and sentiment analysis in recent years. These studies have studied various methods
and practical applications for automatic language analysis and emotional state detection. The
literature review reviews the main theoretical and practical aspects of computational linguistics
and sentiment analysis, and provides some important information about the development and
application possibilities of these fields. Computational linguistics mainly focuses on automatic
language analysis. The first work in this field began in the 1950s and gained momentum in the
following years by the rapid development of deep learning and artificial intelligence
technologies. Studies by Allen (1995), Jurafsky and Martin (2020) provide a lot of important
information about computational linguistics methods, including natural language processing
(NLP), and its practical applications. These methods serve to analyze texts, identify grammatical
structures, and extract semantic meanings [4, 5].
In the world, several studies have been conducted on building a language model and sentiment
analysis based on artificial intelligence algorithms, as well as developing software modules and
tools in the process of natural language processing. The theoretical foundations of sentiment
analysis are described in the works of S.Yu.Toldova, AA.Bonch-Osmolovskaya, T.Sadikov.
Among them, Stanford University professors Christopher Manning, Dan Jurafsky and Percy
Liang (Natural Language Processing Group, USA), University of Southern California professors
Robin Jia and Jesse Thomason (NLP Group, USA), University of Oregon professor Liang Huang
(NLP Group, USA) are conducting research. In addition, the work of Heidelberg University
(Germany) and others in this area has undergone comparative analysis. Stanford University
professor Christopher Manning's contribution to the field of natural language processing has
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shown the effectiveness of advanced linguistic models and machine learning methods, and has
yielded high results in SVM-based sentiment analysis. His research has significantly advanced
the understanding and application of sentiment analysis, emphasizing the importance of complex
algorithms and comprehensive linguistic concepts in correctly analyzing textual data [6]. The
development of sentiment analysis began in the 2000s and quickly gained its practical
application. Pang and Lee (2008) presented the main methods of sentiment analysis, including
word- and phrase-based analysis, in detail. The work of Ian Maas, Ray Yeung, and Richard PL
(2011) on the IMDB dataset is regarded one of the key works in the field of sentiment analysis.
They used deep learning methods such as RNN (recurrent neural networks) and SNN
(convolutional neural networks) [7]. Socher et al. (2013) created the Stanford Sentiment
Treebank and used this dataset for sentiment analysis. They further developed the analysis of
texts with a certain structure using RNN and its variations (e.g. LSTM) [8]. Zadeh et al. (2016)
created the SMU-MOSI dataset and studied multimodal sentiment analysis. They combined text,
image, and audio data and performed multi-factor analysis [9].
While early approaches to sentiment analysis focused on analyzing the emotional value of words,
later deep learning and machine learning technologies played a key role in understanding the full
context of the text (Devlin et al., 2018). Sentiment analysis is one of the most complex problems
in linguistics, as each language has its own semantic and lexical structures [10]. Therefore, cross-
language sentiment analysis research is of great importance. A study by Shen et al. (2018) aimed
to determine the effectiveness of cross-language sentiment analysis and the differences between
different languages. In this study, the issues of how the semantic features of one language affect
another language and how to implement translinguistic analysis in linguistics were investigated
[11].
The study of sentiment analysis in linguistics includes not only semantic and lexical analysis, but
also the social, cultural and pragmatic aspects of language. The research presented by Hoveyda
and Falk (2017) aimed to study the cultural and social context of sentiment analysis [12]. They
showed how sentiment analysis identifies emotional states in each culture and the impact of
different languages and cultures on sentiment analysis. Cross-language (interlingual) studies of
sentiment analysis have been undertaken. In this method, researchers examined the process of
sentiment identification in several languages. The lexical and semantic features of each language
and its cultural context were factored in. Cross-language sentiment analysis analyzed the
differences and similarities of positive and negative words that exist in different languages. A
series of experiments were conducted to measure the effectiveness of sentiment analysis. In these
experiments, models were built based on the above methods to identify different types of
sentiment (positive, negative, neutral) in texts. The results were tested and their accuracy and
efficiency were evaluated [13, 14]. During the experiments, the model was tested taking into
account different lengths, topics, and languages of the texts.
A number of scientific studies on sentiment analysis are also being conducted in the Uzbek
language. For example, S. Matlatipov worked on the topic “Sentiment analysis of the Uzbek
language”. He developed methods for intellectual analysis of the given data to solve the problem
of sentiment analysis of proposals and opinions written in digital text in the Uzbek language [15].
A group of researchers led by Ilyos Rabbimov at Samarkand State University studied the
comments left on Uzbek films based on emojis. S. Allanazarova, a graduate student at the
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National University of Uzbekistan named after Mirzo Ulugbek, discussed the issue of studying
texts from an emotional perspective.
Discussion.
This study investigated computational linguistics and sentiment analysis methods.
The main objective of the study was to improve the efficiency of sentiment analysis and to
identify the most suitable approach by comparing different methods. During the study, various
methods such as word-based analysis, machine learning, deep learning, and cross-language
analysis were tested. The advantages and disadvantages of each method were analyzed, and ways
to improve the efficiency of sentiment analysis were identified. The simplicity and speed of this
method are one of its greatest advantages. However, its disadvantage is that it does not take into
account contextual information in the text. This can sometimes lead to errors, since one word can
have different meanings and emotions in different contexts. Therefore, word-based methods
alone are not enough to improve the accuracy and efficiency of sentiment analysis [16].
Deep learning techniques, such as advanced networks such as RNN, LSTM or Transformer,
provide accurate and efficient implementation of sentiment analysis. They allow for a complete
understanding of the contextual meanings and semantic structure of the text. However, these
techniques require large computational resources and can sometimes be highly complex. Also
known as, training these techniques requires time and effort. Cross-language sentiment analysis
is very useful when working with different languages and can be widely used on a global scale.
These techniques can be used to analyze texts in multiple languages and identify emotional states
in different cultures. However, there are difficulties in taking into account the specific lexical
and semantic features of the language. The unique structure of each language, including
differences between words and phrases, complicates sentiment analysis.
Each method has its own advantages and disadvantages, and when compared with each other,
each works effectively in its own specific situations. Word-based analysis is good for simple
tasks, but deep learning and machine learning methods provide higher accuracy when analyzing
complex and long texts. Cross-language analysis is important when working in different
languages and cultural contexts. For future research, it is necessary to improve the accuracy of
sentiment analysis and develop new approaches to integrating different methods. For example,
combining word-based analysis and machine learning methods can better identify contextual and
lexical features of the text [17]. Also known as, through cross-language research, it is possible to
develop universal sentiment analysis systems in different languages and cultures. In general,
sentiment analysis is an important method that is widely used in the fields of computational
linguistics and linguistics today, and its scientific and practical importance is increasing day by
day. Therefore, it is necessary to conduct more in-depth research in this area and introduce
advanced technologies, which will not only improve the process of understanding language, but
also allow for its effective use in many social and economic areas.
Abstract.
This study aims to study various methods in the field of computational linguistics and
sentiment analysis and compare their effectiveness. The study extensively analyzed methods
such as word-based analysis, machine learning, deep learning, and cross-language analysis. The
advantages and disadvantages of each method were identified and their effectiveness in different
situations was shown. Word-based analysis methods, although they work quickly and easily, do
not take into account the context of the text, and therefore can sometimes lead to errors. Machine
learning methods provide accurate results based on more data, but they require a large amount of
training data for their effective operation. Deep learning methods, in particular Transformer
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networks, are very effective in analyzing the exact and complete context of texts, providing high
accuracy, but these methods require a lot of computational resources. Cross-language sentiment
analysis is effective in working with multiple languages and allows us to take into account
different cultures, but it may face some difficulties in taking into account differences between
languages. The results of the study show that the effectiveness of sentiment analysis depends on
the chosen method and the characteristics of the text being analyzed. Therefore, using a
combination of different methods helps to increase the accuracy of sentiment analysis. In future
research, it is possible to create more effective sentiment analysis systems by integrating
methods and developing new approaches, in particular, by jointly applying cross-language
analysis and deep learning technologies.
List of used literature
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Ismoilov, R. (2020). Computational Linguistics: Theoretical Foundations and
Practical Applications. Academy of Sciences of the Republic of Uzbekistan.
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Sattorov, F. (2018). Methodology of text processing and sentiment analysis.
Linguistics and Computational Linguistics, 12(3), 45-59.
3.
Azimov, M., & Akhmedov, S. (2021). Linguistics and sentiment analysis: New
approaches and research. In Uzbek Linguistics Journal 15(2), 118-134.
4.
Allen, J. (1995). Natural Language Understanding. Addison-Wesley.
5.
Jurafsky, D., & Martin, JH (2020). Speech and Language Processing: An Introduction
to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson.
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G.S.Matlatipov. SENTIMENTAL ANALYSIS
OF THE UZBEKISTAN
LANGUAGE. Abstract. 6 p.
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Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and
Christopher Potts. (2011).
Learning Word Vectors for Sentiment Analysis.
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Meeting of the Association for Computational Linguistics (ACL 2011).b
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Richard Socher, Alex Perelygin, Jean Y. Wu, Jason Chuang, Christopher D. Manning,
Andrew Y. Ng and Christopher Potts (2013).
Recursive Deep Models for Semantic
Compositionality Over a Sentiment Treebank.
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Zadeh, A., Chen, M., Liang, J., Poria, S., & Morency, LP (2016). CMU-MOSI: A
Multimodal Sentiment Analysis Dataset.
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Bidirectional Transformers for Language Understanding. NAACL-HLT.
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Shen, D., Hu, Y., & Sun, Y. (2018). Cross-lingual Sentiment Analysis: A Survey.
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IEEE Access, 6, 10506-10516.
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, Ł.,
& Polosukhin, I. (2017). Attention is All You Need. NIPS.
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Davronov, A. (2020). Application of Computational Linguistics and Sentiment
Analysis in the Uzbek Language. Uzbekistan Institute of Science and Technology.
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