Terms Of Frequency Research Articles

Understanding the dynamics of national business culture: a stationarity analysis for the case of South Korea

ABSTRACT Understanding national business culture – and its subsequent influence on organizational practice – continues to draw scholarly attention. However, being drawn primarily from static, comparative taxonomies, existing research cannot adequately explain and predict the process of cultural evolution (e.g. towards convergence, divergence, or crossvergence). In addressing this caveat, this paper proposes a novel way of understanding changing national business culture using stationarity analysis based on shifting frequency, sentiment, and meanings of representative key terms. Using the big data keywords collected from the Maekyung Daily published over three decades (1991–2021) in Korea, our findings show that cultural changes can be measured based on the four dimensions of ‘conscious vs. unconscious’ on the one hand and ‘political vs. economic’ on the other. Further, we show how the economic and the unconscious dimensions have changed more rapidly than the political and the conscious, reflecting an informal model of cultural changes. We then discuss the practical implications of these findings for MNEs in the cross-border design and implementation of strategy, policy, and process, providing valuable insights for their operations in diverse cultural contexts.

A Domain-Specific Lexicon for Improving Emergency Management in Gas Pipeline Networks through Knowledge Fusing

Emergencies in gas pipeline networks can lead to significant loss of life and property, necessitating extensive professional knowledge for effective response and management. Effective emergency response depends on specialized knowledge, which can be captured efficiently through domain-specific lexicons. The goal of this research is to develop a specialized lexicon that integrates domain-specific knowledge to improve emergency management in gas pipeline networks. The process starts with an enhanced version of Term Frequency–Inverse Document Frequency (TF-IDF), a statistical method used in information retrieval, combined with filtering logic to extract candidate words from investigation reports. Simultaneously, we fine tune the Chinese Bidirectional Encoder Representations from Transformers (BERT) model, a state-of-the-art language model, with domain-specific data to enhance semantic capture and integrate domain knowledge. Next, words with similar meanings are identified through word similarity analysis based on standard terminology and risk inventories, facilitating lexicon expansion. Finally, the domain-specific lexicon is formed by amalgamating these words. Validation shows that this method, which integrates domain knowledge, outperforms models that lack such integration. The resulting lexicon not only assigns domain-specific weights to terms but also deeply embeds domain knowledge, offering robust support for cause analysis and emergency management in gas pipeline networks.

Drone-Assisted Multimodal Logistics: Trends and Research Issues

This study explores the evolving trends and research issues in the field of drone-assisted multimodal logistics over the past two decades. By employing various text-mining techniques on related research publications, we identify the most frequently investigated topics and research issues within this domain. Specifically, we utilize titles, abstracts, and keywords from the collected studies to perform both Latent Dirichlet Allocation techniques and Term Frequency-Inverse Document Frequency analysis, which help in identifying latent topics and the core research themes within the field. Our analysis focuses on three primary categories of drone-assisted logistics: drone–truck, drone–ship, and drone–robot systems. The study aims to uncover which latent topics have been predominantly emphasized in each category and to highlight the distinct differences in research focuses among them. Our findings reveal specific trends and gaps in the existing literature, providing a clear roadmap for future research directions in drone-assisted multimodal logistics. This targeted analysis not only enhances our understanding of the current state of the field but also identifies critical areas that require further investigation to advance the application of drones in logistics.

Ethical dimensions of generative AI: a cross-domain analysis using machine learning structural topic modeling

Purpose The purpose of this study is to comprehensively examine the ethical implications surrounding generative artificial intelligence (AI). Design/methodology/approach Leveraging a novel methodological approach, the study curates a corpus of 364 documents from Scopus spanning 2022 to 2024. Using the term frequency-inverse document frequency (TF-IDF) and structural topic modeling (STM), it quantitatively dissects the thematic essence of the ethical discourse in generative AI across diverse domains, including education, healthcare, businesses and scientific research. Findings The results reveal a diverse range of ethical concerns across various sectors impacted by generative AI. In academia, the primary focus is on issues of authenticity and intellectual property, highlighting the challenges of AI-generated content in maintaining academic integrity. In the healthcare sector, the emphasis shifts to the ethical implications of AI in medical decision-making and patient privacy, reflecting concerns about the reliability and security of AI-generated medical advice. The study also uncovers significant ethical discussions in educational and financial settings, demonstrating the broad impact of generative AI on societal and professional practices. Research limitations/implications This study provides a foundation for crafting targeted ethical guidelines and regulations for generative AI, informed by a systematic analysis using STM. It highlights the need for dynamic governance and continual monitoring of AI’s evolving ethical landscape, offering a model for future research and policymaking in diverse fields. Originality/value The study introduces a unique methodological combination of TF-IDF and STM to analyze a large academic corpus, offering new insights into the ethical implications of generative AI across multiple domains.

Comparing logistic regression and extreme gradient boosting on student arguments

<p>Identifying the effectiveness level and quality of students' arguments poses a challenge for teachers. This is due to the lack of techniques that can accurately assist in identifying the effectiveness and quality of students' arguments. This research aims to develop a model that can identify effectiveness categories in students' arguments. The method employed involves the logistic regression+XGBoost algorithm combined with separate implementations of term frequency-inverse document frequency (TF-IDF) and CountVectorizer. Student argument data were collected and processed using natural language processing techniques. The research results indicate that TF-IDF outperforms in identifying effectiveness classes in student arguments with an accuracy of 66.20%. The multi-output classification yielded an accuracy of 89.32% in the initial testing, which further improved to 92.34% after implementing one-hot encoding. A novel finding in this research is the superiority of TF-IDF as a technique for identifying effectiveness classes in student arguments compared to CountVectorizer. The implications of this research include the development of a model that can assist teachers in identifying the effectiveness level of students' arguments, thereby improving the quality of learning and enhancing students' argumentative competence.</p>

The development of a model for monitoring the quality of online psychological and educational services by using automated analysis of user reviews

Introduction. Digitalization of education and healthcare is an inevitable response to the demands of society. However, it is associated with the emergence of many risks, and the need for regulation and evaluation of new online practices is becoming increasingly important. Questions about the quality of online services are becoming more and more significant, as well as the search for new methods to monitor large amounts of publicly available data about online interactions with helping professionals. The purpose of this study is to develop a model for monitoring psychological and educational online services through automated analysis of user reviews and feedback. Materials and methods. The sample for the study consisted of a variety of reviews about online psychological and educational services. A total of 24,351 reviews of psychologists' work and 6,812 reviews of tutors' work were uploaded. These reviews were ranked in order to identify levels of user satisfaction. We used the following methods to collect and analyze the data: 1) Automated data parsing to extract relevant feedback using the GraphQL API; 2) The TF-IDF (term frequency-inverse document frequency) method to identify the most important linguistic markers in the reviews; 3) The LDA (latent Dirichlet allocation) method for thematic modeling of the reviews. The results of the study. An empirical study of online reviews of psychological and educational services in accordance with this model revealed their thematic structure in four main areas: assessment of interaction (19.8%), assessment of results (26%), organization of professional interaction (20.4%), and assessment of professional skills relevant to clients (29.1%).The main characteristics that users perceive in psychological online services are: the importance of comfortable communication (21.9%), inclusiveness (13.9%), and specialist experience (5.9%). Additionally, the ability to structure a request is also significant (19.8%). The result of evaluating the psychologist's work can be seen in user reviews. Some of the most common responses include: understanding the problem (18.5%), seeing the issue from a different perspective (6.7%), and feeling better (11.3%). Assessments of the quality of online education services include accessibility and clarity of presentation (29.5%), as well as confidence and flexibility in communication (6.1%). Additionally, finding a common language with students is also important (6.2%). Conclusion. The analysis of user feedback on educational and psychological online services can help to understand the directions for the development of work by helping specialists, create a system for monitoring non-specialized assistance, describe criteria for the training and retraining of specialists, and create educational programs for developing digital competence among psychologists and educators.

Psycho-semantic analysis of job advertisements as a communicative tool of educational marketing

Introduction. The relevance of the study of job advertisements for teachers and psychologists is associated with the need to find tools for regulating communication within the modern educational environment with dynamic, constantly changing working conditions. The publication of employer requirements sets the direction for changing the criteria for the success of the specialist, which changes the entire educational environment. The purpose of the study: to identify the categorical semantic structure of job advertisements for teachers (using the example of the "Infrastructure of Scientific Research Data" platform). Materials and methods. The object of the study was a corpus of teacher job vacancies from the "Infrastructure of Scientific Research Data" platform, published in 2023 in 83 regions of Russia (5561 vacancies). The job postings included descriptions of candidate requirements, information about the offered salary, and the region in which the vacancy was available. As a comparison group, a corpus of psychologist job vacancies (2076 vacancies) was considered. The following methods were used in the study: 1) Latent Dirichlet Allocation (LDA) method for identifying and analyzing the structure of requirements presented in teacher and psychologist job vacancies; 2) Term Frequency-Inverse Document Frequency (TF-IDF) method for categorical semantic analysis of the structure of teacher and psychologist job vacancies and description of the semantic framework (frame) of the job description; 3) Descriptive statistical methods for analyzing the needs of professionals in helping professions in different regions of Russia and the conditions offered to them. The results of the study. The regions of the Russian Federation with the greatest need for teachers and psychologists have been identified. Based on the statistical significance in the collection of text documents, the most demanded skills of teachers by employers are identified: methodological development and design of an individual educational route (0.167), work with children with disabilities (0.144), organization of the educational process (0.089). For vacancies of psychologists, the most significant were: work on investigative actions (0.319), methodical work in an educational organization (0.187), psychological and pedagogical support for successful development and training (0.129). The main meanings governing professional pedagogical activity are more focused on meta-skills based on the values of inclusion and continuous improvement of education, as well as on flexible skills (Soft skills) that increase competitiveness in various fields of pedagogical activity. In the vacancies of psychologists, professional skills (Hard skills) are relevant, applicable in specific areas, which somewhat narrows the scope of professional implementation. Conclusion. The results of the empirical study allow for considering job vacancy advertisements as one of the communicative tools of educational marketing. Analyzing the significant professional skills from the employer's perspective will enable the development of recommendations for the development of professional communication for teachers, which can be considered in the system of continuous professional education, as well as in regulating interaction within the educational environment. Automated analysis of job vacancies can become a successful interactive diagnostic tool for educational marketing, necessary for the development of the educational space and the improvement of the competitiveness of educational institutions.

Performance of TF-IDF for text classification reviews on Google Play Store: Shopee

TF-IDF is a technique used to extract features in the field of text classification. The TF-IDF approach extracts feature by considering the frequencies of terms and their inverse document frequencies. The performance of various feature extraction methods varies, and it is necessary to determine the most appropriate approach for accurately classifying Shopee's application user reviews to enhance the user experience in Malaysia. This study aims to assess the efficacy of TF-IDF in text classification tasks, analyze their advantages and disadvantages, and identify the specific conditions in TF-IDF. The study employs a dataset of Shopee customer reviews acquired from the Google Play Store as the main data source. The methodology entails pre-processing the text data by applying a text normalization procedure that includes several processes, such as eliminating stop words, Unicode characters, and lemmatizing. The Logistic Regression, Support Vector Machine, and Decision Tree classifiers are trained and graded using both feature extraction approaches. The research notes that the efficacy of feature extraction approaches may differ based on the specific data set and task being considered. Subsequent studies might examine alternative methods of extracting features and assess their efficacy across various domains and datasets.

Investigation of Exercise Interventions on Postoperative Recovery in Lung Cancer Patients: A Qualitative Study Using Web Crawling Technology.

Rapid recovery after lung cancer surgery is challenging. Exercise is a low-cost, effective method to expedite recovery. Despite numerous exercise interventions, many fail to consider patient perspectives, leading to low adherence and short-term effects. Understanding lung cancer patients' perspectives on postoperative exercise and exploring their exercise-related concerns and needs are crucial for enhancing the effectiveness of exercise-based rehabilitation programs. This study aims to analyze lung cancer patients' perspectives on postoperative exercise in their daily lives, exploring their concerns and needs related to postoperative exercise to help healthcare professionals develop personalized exercise plans. An internet crawling technique collected online inquiries from Baidu webpages about postoperative physical activity in lung cancer patients, using "lung cancer", "surgery", and"exercise" as keywords. The data was encoded, categorized, and analyzed using a large-scale semantic analysis platform in natural language processing and information retrieval to examine term frequency, sentiment tendencies, and attributes in the inquiry texts. Initially, 2727 queries were retrieved; after screening, deduplication, and cleansing, 201 unique queries were identified. Queries related to "modes of exercise" constituted the largest proportion. The most frequently occurring words in the word frequency analysis were "lung", "cancer", "should", "can", "long", "early", and "surgery", "exercise", "respiratory". Postoperative lung cancer patients demonstrate significant interest in whether they should engage in exercise, as well as in the appropriate types and duration of such activities, indicating a strong need for detailed guidance and knowledge related to exercise. The sentiment analysis showed a positive score of 87.5% and a negative score of 12.5%, indicating that postoperative lung cancer patients view exercise positively and have an enthusiastic attitude towards it. Among the positive sentiment attributes, "good" was the most frequently mentioned term, whereas "bad" and "surprising" were the most prevalent terms within the negative sentiment attributes. Postoperative physical activity receives limited attention from lung cancer patients, who emphasize their preferences for exercise modalities. Their inquiries often reflect psychological concerns, such as fear and helplessness caused by symptoms. Understanding patients' perspectives on postoperative physical activity within their real-life contexts can help integrate psychological support into exercise plans. This integration could guide healthcare professionals in developing more personalized postoperative exercise regimens for lung cancer patients.

Application of text mining in analysing notes to financial statements: A Hungarian case

Company stakeholders must have reliable and accurate information about the companies falling into their sphere of interest. In Hungary, one of the key sources of information for company stakeholders is the financial statements and related explanations, which are included in the notes of the financial statements (notes). This study used text mining to analyse the Hungarian annual financial statements notes for 2017, 2019 and 2021. The selection of the notes was based on the proportions of each sector in the national economy. The research analysed 28,700 company notes annually, totalling 86,100 documents for the three years. The text mining and generation of the Term Frequency Matrix have performed 'quanteda' packages of the R statistical system, which incorporate the results of artificial intelligence research to enhance the efficiency of text mining. Based on the results, the contents of the notes to the financial statements appear to be a rather mixed picture in Hungary. Analysing the term frequency matrix for the 67 most common terms has revealed no significant difference between the years. However, considerable differences have been caused by size categories and sectors. The notes are statistically significant using Jaccard similarity analysis, considering the year, corporate size, and sector.

A META ANALYSIS OF NOMINAL PHRASES WITH DRAMA AS HEAD NOUN AND MODIFIER

This study conducts a meta-analysis of nominal phrases with "drama" as the head noun and modifier, aiming to clarify the terminology within the field of drama education and address discrepancies in term usage across various linguistic contexts. Utilizing a quantitative approach, the research analyzes the frequency and usage patterns of drama-related terms in the British National Corpus (BNC) and Google Scholar. Significant disparities were found, indicating broader usage and diverse applications in Google Scholar compared to the BNC. The study highlights the need for standardized terminology to enhance communication and comparability in academic research. By defining key terms such as "drama activity," "drama play," and "educational drama," the analysis contributes to reducing ambiguities and fostering a more coherent dialogue within drama studies, thereby facilitating more effective educational practices and interdisciplinary collaboration.

Anger, fear, and frozenness: Exploring the emotive aspect of anti-police sentiment

Anti-police sentiment has emerged as a growing phenomenon in recent years, characterised by intense negative attitudes towards police. Despite increasing scholarly interest, the lack of a clear definition of anti-police sentiment hinders conceptualisation, measurement, and comparison of findings across jurisdictions. This study aims to provide conceptual clarity by employing sentiment analysis to systematically and objectively explore the emotions underlying anti-police sentiment. Analysis of the negative discourse across 1140 comments on Reddit about police, found that anti-police sentiment is associated with the emotions anger, fear, and frozenness, and potentially may trigger fight, flight, freeze responses in citizens encountering the police. Further, analysis of frequent terms highlights key social issues driving anti-police sentiment, such as police responses to domestic violence and police misconduct. The study contributes to the understanding of anti-police sentiment by identifying the underlying emotions and their potential behavioural outcomes, providing a foundation for future research and policy interventions.

Analysis of Guangzhou city image perception based on weibo text data (2019–2023)

With the popularization of smart mobile terminals and social media, a large amount of data containing textual information about the city has been generated on social media platforms, covering all areas of the city. This provides a new way for the study of comprehensive perception of city image. In the Internet era, users express their opinions about cities through social media platforms (e.g., Sina Weibo), and mining this information helps to understand the image of cities on mainstream social media and to target positive images to improve the competitiveness of the city's image. In this paper, 370,000 microblog messages related to “Guangzhou City” between 2019 and 2023 are collected using web crawler technology, and three typical text analysis methods are adopted: Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Sentiment Analysis (SnowNLP), to understand the characteristics of Guangzhou city image. gain an in-depth understanding of Guangzhou's urban image characteristics. The study shows that extensive data analysis methods based on text mining can perceive the dynamics and trends of the city in a timely manner, refine the characteristics of Guangzhou's urban image, and propose communication strategies for Guangzhou's image. This study aims to mine Guangzhou's urban image presented on Weibo, provide data support for relevant departments in China and Guangzhou to formulate communication strategies, and provide references for other cities to manage their urban image.

TF-IDF combined rank factor Naive Bayesian algorithm for intelligent language classification recommendation systems

With the continuous improvement of smart language systems, a large amount of language text data has emerged. How to efficiently and accurately process these text data has become an important challenge. Therefore, a speech classification recommendation system based on improved Naive Bayesian algorithm is proposed. The system first adopts the traditional Bayesian algorithm to classify language texts. The Term Frequency-Inverse Document Frequency and rank factor are combined to increase the weight of feature languages. Then, the classified language texts are combined with the improved algorithm for language classification recommendation. Finally, performance testing and simulation applications are conducted on the system. From the results, in the Gutenberg corpus, the research algorithm had the highest accuracy and completeness, with 98.5 % and 91.6 %, respectively, and the lowest values were 92.6 % and 89.4 %. The average values were 95.5 % and 91.1 %, with an F1 value of about 92.6 %. In the Brown corpus, the average accuracy, completeness, and F1 value of the designed algorithm were 96.2 %, 91.2 %, and 93.2 %, respectively. When the number of online customers reached 1000, the response time of the designed Chinese system was 1.15 s, the classification recommendation accuracy was 95 %, and the system stability was about 83 % on average. The response time of the English system was 0.64 s, the classification recommendation accuracy was 96 %, and the system stability was about 90 % on average. It shows that the designed method can significantly enhance the operation accuracy of the classification recommendation system.

تأثير طرق تحويل السمات على البيانات العربية الكبيرة باستخدام تصنيف االنحدار اللوجستي

The process of assigning text documents to a predetermined set of categories is known as text categorization. The objective of this study is to present experimental assessments of various feature vectorization methods for the purpose of categorizing a large Arabic corpus using a logistic regression classifier. N-Gram, Bag of Words, and Term Frequency–Inverse Document Frequency are these methods. A corpus of around 111,000 Arabic documents was utilized, which was split up into five categories: news, sports, culture, economics, and varied. Each method's experimental findings were assessed using three different performance indicators. According to the experimental findings, the Logistic Regression model using Term Frequency–Inverse Document Frequency and N-gram (1,2) had the best accuracy, scoring 96%, while Bag of Words came in second with 95%.

AI Integration and Economic Divides: Analyzing Global AI Strategies

Abstract This study investigates the impact of socio-economic factors on national AI strategies in India, Bangladesh, Germany, UAE, Egypt, and the USA through quantitative content analysis. The analysis explores the correlation between GDP per capita, the share of manufacturing, and the frequency of risk-related terms in AI strategy documents. It is found that wealthier nations emphasize AI risks more, correlating with deeper technological integration into their societal structures. Conversely, the emphasis on AI risks shows a weak correlation with the share of manufacturing, indicating broader AI impacts in service-oriented sectors. Lower-middle-income countries appear more optimistic, focusing on AI's economic benefits. The study underscores the need for balanced AI strategies that promote innovation while ensuring worker well-being, advocating for adaptive governance frameworks that enhance workplace safety and efficiency.

Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity

Document similarity is a fundamental task in natural language processing and information retrieval, with applications ranging from plagiarism detection to recommendation systems. In this study, we leverage the term frequency-inverse document frequency (TF-IDF) to represent documents in a high-dimensional vector space, capturing their unique content while mitigating the influence of common terms. Subsequently, we employ the cosine similarity metric to measure the similarity between pairs of documents, which assesses the angle between their respective TF-IDF vectors. To evaluate the effectiveness of our approach, we conducted experiments on the Document Similarity Triplets Dataset, a benchmark dataset specifically designed for assessing document similarity techniques. Our experimental results demonstrate a significant performance with an accuracy score of 93.6% using bigram-only representation. However, we observed instances where false predictions occurred due to paired documents having similar terms but differing semantics, revealing a weakness in the TF-IDF approach. To address this limitation, future research could focus on augmenting document representations with semantic features. Incorporating semantic information, such as word embeddings or contextual embeddings, could enhance the model's ability to capture nuanced semantic relationships between documents, thereby improving accuracy in scenarios where term overlap does not adequately signify similarity.

A Text-Inception-Based Natural Language Processing Model for Sentiment Analysis of Drug Experiences

The study of sentiment in Natural Language Processing (NLP) is among the most successful research areas because of the availability of millions of user opinions online since the turn of the century. The economic, political, and medical fields are just some of the many that have benefited from studies of sentiment research. While numerous studies have examined more mainstream topics like consumer electronics, movies, and restaurants, relatively few have examined health and medical concerns. Considerable insight into where to direct efforts to improve public health might be gained by a study of how people feel about healthcare as a whole and of individual drug experiences in particular. When it comes to medicine, automatic analysis of online user evaluations paves the way for sifting through massive amounts of user feedback to find information regarding medications' efficacy and side effects that might be used to enhance pharmacovigilance programs. Simple rules-based methods have given way to more complex machine learning approaches like deep learning, which is developing as a technology for many natural language processing jobs. The opensource datasets have been analyzed with models that use word embeddings and term frequency-inverse document frequency (TF-IDF). A feature-enhanced text-inception model for sentiment classification was presented to work in tandem with this approach. The model first employed a cutting-edge text-inception module to glean useful shallow features from the text. K-MaxPooling was subsequently employed to reduce the dimensionality of its shallow and deep includes as well as enhance the generalization of characteristics, and a deep feature extraction module was formed using the bidirectional gated recurrent unit (Bi-GRU) and the capsule neural network to comprehend the text's semantic data. By combining traditional methods with cutting-edge artificial intelligence techniques, this hybrid approach can revolutionize public health initiatives, decision-making, and pharmacovigilance in the healthcare industry. This model achieved an exceptional accuracy rate of 99%, underscoring its effectiveness in sentiment classification and demonstrating its potential to significantly contribute to advancing healthcare and medical research.

SENTIMENT ANALYSIS OF ELECTRIC CAR PRODUCT TRENDS IN INDONESIA USING BM25 AND K-NEAREST NEIGHBOR

The global and Indonesian shift towards electric vehicles (EVs) is driven by efforts to reduce emissions and promote sustainable energy. Social media, especially Twitter, functions as an important measuring tool regarding public sentiment towards electric vehicles in Indonesia, so that it can influence policy making. This research uses the BM25 and K-Nearest Neighbor (KNN) methods to analyze sentiment, which aims to improve EV adoption strategies. Conducted in 2023, this research applies data mining, specifically Knowledge Discovery and Data Mining (KDD), analyzing primary and secondary data descriptively and quantitatively starting with data collection from Twitter, followed by data crawling and initial text processing. Next, labeling, term frequency (TF) and inverse document frequency (IDF) calculations were carried out using the BM25 and KNN methods, with an Evaluation and Validation Diagram that visualized the process. The findings show that negative sentiment dominates at 48% (4800 data), followed by 34% (3400 data) neutral sentiment and 18% (1800 data) positive sentiment. The balanced distribution of sentiment highlights the diverse perceptions of society. BM25 and KNN pre-processing methods effectively reduce overfitting and underfitting, especially in negative and neutral sentiments. Accuracy testing without BM25 resulted in 58.6% to 60.25%, while integrating BM25 with KNN increased accuracy by 12.5% to 71% to 72.75%. Understanding sentiment provides a basis for decision making and policy development, as well as providing insight into public perceptions of electric vehicles in Indonesia. Implications include leveraging positive sentiment for marketing, adjusting strategies, refining pricing, addressing infrastructure and reliability issues, and collaborating with governments to increase adoption of electric vehicles in society.

Clustering and Analysis of Tweets Related to Petrobras

This study aimed to cluster and analyze tweets associated with Petrobras, exploring its meaning and user profiles on social media to understand their impact on financial markets. The research applied a workflow including the data collection from Twitter's API (current X), preprocessing of tweets using Python libraries, word vectorization via Bag-of-Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), Principal Component Analysis (PCA) to reduce matrix dimensionality, and the K-means clustering technique. A total of 840 preprocessed tweets were clustered and analyzed for patterns related to Petrobras. Five clusters were identified in the initial analysis with no dimensionality reduction, showcasing differing characteristics, while the subsequent PCA-based analysis yielded three clusters showing contrasting themes in tweets. The PCA-based analysis showed grouped tweets about the market and economy (cluster 0), while cluster 1 was related to political concerns. Limitations included reliance on publicly available Twitter data, constraints due to the quantity and nature of tweets, and potential biases in sentiment analysis due to informal language and sarcasm. The research underscores the potential of unsupervised machine learning techniques in analyzing sentiments and user profiles related to financial markets. Insights derived from tweet clustering could aid investors in gauging market sentiment.