Investigating the Effect of Emoji in Opinion Classification of Uzbek Movie Review Comments

Opinion mining on social media posts has become more and more popular. Users often express their opinion on a topic not only with words but they also use image symbols such as emoticons and emoji. In this paper, we investigate the effect of emoji-based features in opinion classification of Uzbek texts, and more specifically movie review comments from YouTube. Several classification algorithms are tested, and feature ranking is performed to evaluate the discriminative ability of the emoji-based features.Comment: 10 pages, 1 figure, 3 table

On the development of an information system for monitoring user opinion and its role for the public

Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users' opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem's social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system's study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people's moods and attitudes to the Government's policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood

Sentiment Classification of Russian Texts Using Automatically Generated Thesaurus

This paper is devoted to an approach for sentiment classification of Russian texts applying an automatic thesaurus of the subject area. This approach consists of a standard machine learning classifier and a procedure embedded into it, that uses the- saurus relationships for better sentiment analysis. The thesaurus is generated fully automatically and does not require expert’s involvement into classification process. Experiments conducted with the approach and four Russian-language text corpora, show effectiveness of thesaurus application to sentiment classification

Multiethnic Societies of Central Asia and Siberia Represented in Indigenous Oral and Written Literature

Central Asia and Siberia are characterized by multiethnic societies formed by a patchwork of often small ethnic groups. At the same time large parts of them have been dominated by state languages, especially Russian and Chinese. On a local level the languages of the autochthonous people often play a role parallel to the central national language. The contributions of this conference proceeding follow up on topics such as: What was or is collected and how can it be used under changed conditions in the research landscape, how does it help local ethnic communities to understand and preserve their own culture and language? Do the spatially dispersed but often networked collections support research on the ground? What contribution do these collections make to the local languages and cultures against the backdrop of dwindling attention to endangered groups? These and other questions are discussed against the background of the important role libraries and private collections play for multiethnic societies in often remote regions that are difficult to reach

Multiethnic Societies of Central Asia and Siberia Represented in Indigenous Oral and Written Literature

Central Asia and Siberia are characterized by multiethnic societies formed by a patchwork of often small ethnic groups. At the same time large parts of them have been dominated by state languages, especially Russian and Chinese. On a local level the languages of the autochthonous people often play a role parallel to the central national language. The contributions of this conference proceeding follow up on topics such as: What was or is collected and how can it be used under changed conditions in the research landscape, how does it help local ethnic communities to understand and preserve their own culture and language? Do the spatially dispersed but often networked collections support research on the ground? What contribution do these collections make to the local languages and cultures against the backdrop of dwindling attention to endangered groups? These and other questions are discussed against the background of the important role libraries and private collections play for multiethnic societies in often remote regions that are difficult to reach

WSN based sensing model for smart crowd movement with identification: a conceptual model

With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way

Ontology model for zakat hadith knowledge based on causal relationship, semantic relatedness and suggestion extraction

Hadith is the second most important source used by all Muslims. However, semantic ambiguity in the hadith raises issues such as misinterpretation, misunderstanding, and misjudgement of the hadith’s content. How to tackle the semantic ambiguity will be focused on this research (RQ). The Zakat hadith data should be expressed semantically by changing the surface-level semantics to a deeper sense of the intended meaning. This can be achieved using an ontology model covering three main aspects (i.e., semantic relationship extraction, causal relationship representation, and suggestion extraction). This study aims to resolve the semantic ambiguity in hadith, particularly in the Zakat topic by proposing a semantic approach to resolve semantic ambiguity, representing causal relationships in the Zakat ontology model, proposing methods to extract suggestion polarity in hadith, and building the ontology model for Zakat topic. The selection of the Zakat topic is based on the survey findings that respondents still lack knowledge and understanding of the Zakat process. Four hadith book types (i.e., Sahih Bukhari, Sahih Muslim, Sunan Abu Dawud, and Sunan Ibn Majah) that was covering 334 concept words and 247 hadiths were analysed. The Zakat ontology modelling cover three phases which are Preliminary study, source selection and data collection, data pre-processing and analysis, and development and evaluation of ontology models. Domain experts in language, Zakat hadith, and ontology have evaluated the Zakat ontology and identified that 85% of Zakat concept was defined correctly. The Ontology Usability Scale was used to evaluate the final ontology model. An expert in ontology development evaluated the ontology that was developed in Protégé OWL, while 80 respondents evaluated the ontology concepts developed in PHP systems. The evaluation results show that the Zakat ontology has resolved the issue of ambiguity and misunderstanding of the Zakat process in the Zakat hadith. The Zakat ontology model also allows practitioners in Natural language processing (NLP), hadith, and ontology to extract Zakat hadith based on the representation of a reusable formal model, as well as causal relationships and the suggestion polarity of the Zakat hadith

Livelihoods and Social-Environmental Change in the Syr Darya Delta: Adaptive Strategies and Practices

In this dissertation, I examine how local communities in the Syr Darya River delta (Kazakhstan) have been adapting to rapid social-environmental change. While the current environmental change discourses in the study area overwhelmingly focus on the desiccation of the Aral Sea and its consequences, I focus on the Syr Darya Delta, which represents an interesting case of the rural livelihoods such as herding, fishing, reed mowing, and gardening used by locals as common-pool resources. This case is particularly interesting against the backdrop of the Aral Sea catastrophe, which is often regarded as a classic example of the tragedy of commons. I also draw the reader’s attention to the ontological aspects of the environmental change and describe iconic places, iconic species and iconic forces that shaped the irrigation infrastructure development in the Syr Darya Delta. By analyzing the rural livelihoods in the Syr Darya Delta from the commons perspective, I demonstrate some of the limitations of the commons’ institutional design principles and explain why the tragedy of the commons did not happen in the Syr Darya Delta. I further analyze these livelihoods from commoning perspective and make contributions to the commons literature by highlighting how cooperation and competition unfold concurrently and how the cooperation varies across different steps of commoning. Then, I present an overview of the various adaptation strategies used by the local communities in the Syr Darya Delta. I demonstrate that the commonization of resources served as an overarching adaptation strategy, i.e., former state-owned lands and resources have been turned into de-facto common-pool resources. I also describe other adaptive strategies such as shifting agriculture, local soil knowledge, local irrigation techniques and demonstrate how rice-growing corporations can afford a wider range of coping strategies as opposed to a small-scale commoners

Integrating Distributional, Compositional, and Relational Approaches to Neural Word Representations

When the field of natural language processing (NLP) entered the era of deep neural networks, the task of representing basic units of language, an inherently sparse and symbolic medium, using low-dimensional dense real-valued vectors, or embeddings, became crucial. The dominant technique to perform this task has for years been to segment input text sequences into space-delimited words, for which embeddings are trained over a large corpus by means of leveraging distributional information: a word is reducible to the set of contexts it appears in. This approach is powerful but imperfect; words not seen during the embedding learning phase, known as out-of-vocabulary words (OOVs), emerge in any plausible application where embeddings are used. One approach applied in order to combat this and other shortcomings is the incorporation of compositional information obtained from the surface form of words, enabling the representation of morphological regularities and increasing robustness to typographical errors. Another approach leverages word-sense information and relations curated in large semantic graph resources, offering a supervised signal for embedding space structure and improving representations for domain-specific rare words. In this dissertation, I offer several analyses and remedies for the OOV problem based on the utilization of character-level compositional information in multiple languages and the structure of semantic knowledge in English. In addition, I provide two novel datasets for the continued exploration of vocabulary expansion in English: one with a taxonomic emphasis on novel word formation, and the other generated by a real-world data-driven use case in the entity graph domain. Finally, recognizing the recent shift in NLP towards contextualized representations of subword tokens, I describe the form in which the OOV problem still appears in these methods, and apply an integrative compositional model to address it.Ph.D

The Ludic Life of Things: Explorations in the Vitality of the Ludic Object in Contemporary Narratives

This project makes an object-oriented analysis of the activity and vitality of the material culture present within contemporary American narratives, with a specific focus on the materiality of the ludic. Drawing upon new ideas of Being developed by Object-Oriented philosophers, this work aims to divert literary analysis’s focus from the non-material, Jamesonian mode of the ‘Political Unconscious’ to a re-energized grappling with the ‘Material Unconscious’ that pervades contemporary texts. As part of this grappling, the work looks at both traditional literary texts and video game and television narratives so as to better understand the activity of the narrative medium itself as ludic object. In analyzing Don DeLillo\u27s Underworld, Square Enix\u27s The World Ends With You, and Peter Berg\u27s Friday Night Lights through this lens, it reveals the manner in which the ludic object vitally interacts so as to produce and negotiate an unconscious Utopian impulse within the texts