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Qadın sahibkarlarda Transformativ, Strateji və Xidmətə yönəlik Liderlik: Azərbaycanlı Nailəxanım İsayevanın Uğur Hekayəsi
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Sentiment Analysis Using Machine Learning Methods on Social Media
No full textSentiment analysis deals with understanding human feelings and opinions by
analyzing the emotional content of words. With the rise of social media platforms,
an immense volume of text data has become available for analysis. Machine
learning (ML) techniques are essential to process this data and can provide
businesses with deep insights into customer feedback, brand reputation, and
emerging market trends. They also help governments and public organizations
gauge public opinion on current events, proposed laws, and social issues. This study
aims to improve the accuracy and effectiveness of sentiment analysis on social
media (specifically Twitter) by applying advanced ML methods to address
challenges of contextual understanding, noisy text preprocessing, and the evolving
slang and vernacular of online content. We developed a sentiment classification
model for Twitter data and evaluated several algorithms on a real-world dataset. The
results show that a ML approach can successfully classify social media posts by
sentiment, highlighting prevailing public moods in real time. In our experiments, an
ensemble model outperformed other classifiers in balancing precision and recall,
achieving high overall accuracy. These findings showcase the potential of ML
methods in capturing the sentiment of social media discourse
Analysis of Aleksander Mardkowicz’s Poem “Hajji Baba“ in the Context of Cultural Anthropology
No full textGloballeşme millî kültürleri etkilemiş, giderek ulusal renk ve ahenklerden uzaklaşmasına neden olmuştur. Karay Türkleri ise bu tehlikeyi 19. yüzyıldan itibaren hissetmeye başlamış, aynı dini inançta oldukları İsrailoğullarından (Yahudiler) farklı bir millî kimliğe sahip olduklarını kanıtlamaya çalışmışlardır. Bu çalışmayı yürütenlerin başında ise Aleksander Mardkowicz’in “Hacı Baba” uzun şiirinin kahramanı Awraham Firkowicz gelmiştir. O, Karay tarihinde ilk antropolog, arkeolog olarak yer almış, çok geç yaşlarında eğitim almasına rağmen İbraniceyi mezar taşlarını araştıracak, Mısır’dan Yerusalim’e kadar elyaz malarını derleyecek ve koleksiyon oluşturacak derecede iyi öğrenmiştir. 1839 yılında Çarlık yönetiminin Karay kimliğiyle ilgili sorularına yanıt aramak için kültürel antropolojik çalışmalarını başlatan ve ömrünün sonuna kadar devam ettiren Firko wicz edindiği sonuçları “Masa uMriva”, “Avne Zikkaron” gibi önemli kitaplarında yayımlamıştır. Onun bilimsel çalışmaları ölümünden sonra büyük tartışmalara yol açmış, Yahudi bilginlerince eleştirilmiştir. Fakat zamanla büyük Karay bilgininin millî kimlikle ilgili tespitleri doğrulanmıştır. “Karay Avazı” dergisinin kurucusu Aleksander Mardkowicz yedi bölümden oluşan “Hacı Baba” adlı uzun şiirinde Awraham Firkowicz’in yaşam öyküsünden bahsetmiş, onu Karayların en ünlü kişilerinden biri olarak övmüştür. Uzun şiirde Karay kimliği sorunu bir daha dile getirilmiş ve özellikle de Rabbanlarla Karayların kıyaslaması yapılmış, aynı zamanda Karay ve Türk kelimeleri aynı “antropolojik küme”de yer almıştır
Synthesis and structure of 6-bromo-2-(diethoxymethyl)- 2-hydroxy-3-phenyl-2,3-dihydro-1Himidazo[ 1,2-a]pyridin-4-ium chloride acetonitrile monosolvate
No full textIn the title solvated molecular salt, C18H22BrN2O3+·Cl−·CH3CN, the imidazole ring is in envelope conformation and the pyridine and phenyl rings are oriented at a dihedral angle of 72.52 (5)°. In the crystal, O—H⋯Cl and N—H⋯Cl hydrogen bonds link the cations and anions into centrosymmetric tetramers enclosing R24(12) loops. Short Br⋯Cl [3.2313 (4) Å] and O⋯Cl [3.0490 (10) Å] contacts are observed. A Hirshfeld surface analysis of the structure indicates that the most important contributions for the crystal packing are from H⋯H (52.4%), H⋯C/C⋯H (12.1%), H⋯Br/Br⋯H (11.0%) and H⋯Cl/Cl⋯H (10.2%) interaction
Crystal structure and Hirshfeld surface analysis of (3aRS,4RS,10SR,10aSR)-2-(3,5-dimethylphenyl)-4- hydroxy-10-methyl-1-oxo-2,3,3a,4,10,10ahexahydro- 1H-[1]benzofuro[2,3-f]isoindole-10- carboxylic acid dimethylformamide monosolvate
No full textThe molecular conformation of the title compound, C24H23NO5·C3H7NO, is consolidated by intramolecular C—H⋯O O—H⋯O hydrogen bonds, forming an S(6) ring motif. In the crystal, the molecules are connected by C—H⋯O hydrogen bonds, forming layers parallel to the (101) plane. Furthermore, the molecules form layers parallel to the (102) plane by C—H⋯π interactions. Important intermolecular interactions highlighted by Hirshfeld surface analysis are H⋯H (54.7%), O⋯H/H⋯O (23.0%), and C⋯H/H⋯C (19.9%) contacts
Sosial mediada maşın öyrənmə metodlarından istifadə edərək sentiment analizin aparılması
No full textFaculty: Graduate School of Science, Art and Technology
Department: Computer Science
Specialty: Informatics
Supervisor: PhD, Associate Professor Leyla Muradkhanli GazanfarSocial media has rapidly transformed into a key platform in which people express thoughts, feelings, as well as reactions towards current events, political decisions, social issues, also commercial experiences. Among those platforms, Twitter distinguishes itself via the relative brevity of the posts as well as the large intensity of more public interaction, making it such a perfect ground for sentiment analysis. In this thesis, the sole focus lies upon extracting meaningful emotional patterns from user-generated content which is on Twitter by using machine learning algorithms. The research scope is not restricted solely to sentiment polarity identification but stretches toward the structural optimisation of sentiment analysis models using Azerbaijani-language data, with specific emphasis upon recent sociopolitical discourse touching aviation incidents.
Instead of the usual surveys and structured feedback forms, Twitter posts show several spontaneous reactions. These reactions, completely unfiltered, contain nothing that acts as a filter. This spontaneity introduces linguistic noise, informal syntax, and wide-ranging usage of abbreviations, emojis, and colloquialisms within the Azerbaijani language, particularly because it lacks adequate annotated corpora for computational analysis. To overcome these limitations, the study adopts a strict preprocessing pipeline that includes spelling normalization, tokenization, removal of stop words, in addition to lemmatization, with careful handling of non-standard text elements. After filtering, the data is vectorized using word embeddings, specifically Word2Vec and BERT. This permits semantic and contextual representation far beyond frequency analysis alone.
The dataset was additionally improved via established stratified sampling techniques so as to ensure balanced representation of sentiments, thereby minimising bias in model training as well as within evaluation. Beyond just classical evaluation metrics, confusion matrices were analysed in a visual way for classification errors and also to help refine the decision boundaries within the models. Attention was paid in addition to temporal trends that are within sentiment expression, revealing shifts in emotional tone during phases that are within public discourse. Language-specific challenges, such as a scarcity of sentiment lexicons and of pre-trained models in Azerbaijani, were reduced via manual annotation as well as domain adaptation strategies. For further improved model generalisation, k-fold cross-validation was applied along with hyperparameter tuning via randomised search.
The machine learning algorithms applied—Logistic Regression, Support Vector Machines, and Random Forest—were completely compared utilising metrics like accuracy, precision, recall, as well as F1-score. Each model was evaluated both on a general sentiment dataset with emotionally tagged keywords such as "love", "hate", "disappointed", as well as on one context-specific dataset of Azerbaijani tweets related to the Aktauda aviation incident. The results do show Random Forest and SVM manage subtle language cues in a better way than Logistic Regression does, especially in irony, blame, or sarcasm, but Logistic Regression is okay in simple contexts. Among these, Random Forest emerged as being the most strong while reaching up to 89% in accuracy. It demonstrated performance balanced across some sentiment classes.
The analysis does also confirm social media posts as expressive indicators for public emotion and function as a lens through which political and cultural narratives then unfold. For instance, tweets about that aviation incident communicated no less than fear and grief, but also politically charged accusations together with calls for accountability. These feelings, when charted, display public trust, anger, or admiration, each varying as a reaction to government and world responses. This confirms the total planned value of sentiment analysis within public policy evaluation, crisis communication, and within media monitoring.
Through integration of domain-specific feature engineering, alongside contextual embeddings, and incorporation of linguistic particularities natural to the Azerbaijani language, this study contributes a methodological framework that is adaptable for many under-resourced languages. It puts forward also a rather dynamic approach for sentiment classification. This approach remains effective within a constantly evolving online vernacular. In contrast to static, lexicon-based models, this ensemble learning method is more responsive to present data, enabling institutions, journalists, and researchers to interpret digital emotions faster and more accurately.
To conclude, the research shows sentiment analysis on Twitter with machine learning is far more than mere computation; it is a socio-technical study of just how societies feel and then voice emotion online. By decoding emotional undercurrents within social media discourse, notably during times of crisis, decision-makers are better equipped to understand collective psychology, respond to misinformation directly, and engage with citizens meaningfully in the era of digitised public opinion
Bridging cultures through explicitation: a corpusbased analysis of bilingual literary translations
No full textThis study examines explicitation strategies in translated literary texts to determine
their impact on the text’s clarity, style, and cultural representation. Focusing on how
these shifts affect the explicitation shifts, the study analyzed two modern Urdu novels,
La Hasil and Aks, and their English translations. Using TagAnt and UA M CorpusTool, the
study examines approximately 30,000 words of each text to determine the frequency
and function of explicitation in the process of constructing the readability and cultural
translatability of the target texts. The results show that obligatory explicitation is the
most common, further improving coherence and grammatical accuracy while
occasionally affecting text-cohesion style. Optional explication, although less typical,
enhances stylistic device productivity and, at the same time, restricts the range of
interpretation by providing contextual meanings of ambiguous words. However,
occasional use of pragmatic explicitation is vital since some relations are culture-bound
and, therefore, likely to be unfamiliar to a non-native English readership. Emphasizing
the need to balance clarity and fidelity in translation work, it offers insights into
translating interlingual literary texts in multinational contexts. Thus, it realizes that
translation is an important object of investigation, which corrects and completes the
esthetic and cultural density of the source text through explicitation
Akademik kommunikasiyada Azərbaycan dilinin yeri
No full text“Azərbaycan dilində işgüzar və akademik kommunikasiya” fənni 2020–2021-ci tədris ilindən etibarən, ali məktəblərin bütün ixtisaslarında tədris olunaraq tələbələrə akademik və işgüzar kommunikasiya üzrə təməl biliklər verir, onları müvafiq bilik və bacarıqlara yiyələndirir. Akademik kommunikasiya şifahi (mühazirə, məruzə, çıxış) və yazılı (dərs vəsaiti, monoqrafiya, dissertasiya, məqalə və s.) olmaqla iki hissəyə ayrılır. Azərbaycan dilinin elmi üslubu kifayət qədər inkişaf etdiyindən mütəxəssislər bu dildə yüksək səviyyədə akademik kommunikasiya qura bilirlər. Ali məktəblərdə istənilən ixtisas üzrə mühazirələr deyilir, elmi konfranslarda məruzələr edilir, dissertasiya və məqalələr yazılır. Bununla yanaşı, ölkəmizin hüdudlarından kənarda da elmi məqalələri yazıldığı dildə çap etdirmək imkanı əldə edilmişdir. Məqalədə şifahi akademik kommunikasiyanın formaları, səciyyəvi xüsusiyyətləri, bir-birindən fərqi, onlara verilən başlıca tələblər açıqlanmış, həmçinin humanitar sahələr üzrə Azərbaycan dilində ödənişsiz olaraq elmi məqalələr qəbul edən və nüfuzlu beynəlxalq bazalarda indekslənən bir neçə xarici jurnal (Türkiyə) barədə məlumat verilmişdir. Tədqiqat zamanı müşahidə, müqayisə və təsviri metodlardan istifadə edilmişdir. Araşdırma ali məktəblərdə pedaqoji fəaliyyətə başlayan gənc müəllimlər, tədqiqatçılar və doktorantlar üçün faydalı ola bilər
Exploring the Relationship Between Critical Thinking and Creativity in University Students: Gender Differences and the Assessment of Skills
No full textThis study aimed to explore the relationship between the critical thinking and
creativity skills of university students. The objectives were to explore the level of critical
thinking skills, the degree of creativity in students’ written exam papers, the nature of the
relationship between these constructs, and gender differences in the manifestation of these
skills in writing. A sample of 167 students, including 100 females and 67 males, from a
co-educational university participated in the study. Data were collected using self-report
measures for critical thinking and creativity from student mid-term exam papers. The
assessment tool to assess critical thinking skills, and an adapted version of the TTCT VerbalA was used to measure the creativity level in the written products. Descriptive statistics,
correlation analysis, and comparative analysis were conducted using SPSS (version 29).
The findings revealed that both the variables’ scores were moderate in the data. They also
indicated that students fall within the moderate level of both the skills. A significant positive
correlation was found between critical thinking and creativity, suggesting a meaningful
relationship between these constructs. Gender differences were also observed, with females
scoring higher in both the constructs compared to males. Furthermore, these insights
highlight the need for educational strategies that foster both skills, ensuring a balanced
development among students. This study is useful for educators, policymakers, and
researchers interested in critical thinking and creativity and also underscores the need for
future research and curricula to enhance student learning outcomes