Negative Categories Research Articles

CO2 Utilization and Market Size Projection for CO2-treated Construction Materials

Achieving a net climate benefit requires that captured CO2 (from stack emissions, directly from air or any other source) does not return to the atmosphere. There are several negative-emissions technologies that will meet this criterion. First, underground storage, possibly coupled with mineralization underground, creates essentially permanent removal. Second, CO2 can be mineralized above ground into benign solid materials that can be left in the environment. Third, construction materials such as concrete, aggregates, and wall boards can be made with the use of CO2. From a climate point of view the lifetime of those materials can be considered permanent. While structures (buildings, roads, etc.) will not last permanently, the mineralized CO2 in the underlying raw materials will. The latter negative emissions technology category has the added benefit of creating revenue from the sale of the products and thus providing financial incentives for deployment. The potential CO2 utilization amount and market size for mineral-based construction materials is projected from today to 2050 and is discussed in context of the total addressable market for respective products. A range of scenarios for growth are considered and discussed. CO2 utilization between 1.0 and 10.8 gigatons per year is projected with a market valuation reaching 0.8–1 trillion USD/year by 2050.

Building a Sustainable Social Feedback Loop: A Machine Intelligence Approach for Twitter Opinion Mining

This paper presents a sustainable machine intelligence approach for Twitter opinion mining, focusing on building a socially responsible feedback loop. We propose a methodology that combines advanced machine learning algorithms with eco-conscious practices to extract sentiment-related insights from Twitter data while minimizing environmental impact. The preprocessing steps involve removing special characters, tokenization, stop word removal, handling user handles and URLs, and lemmatization or stemming. Sentiment classification is performed using the Extra Tree Classifier, an ensemble learning algorithm that incorporates random feature selection and bagging techniques. Experimental results demonstrate the effectiveness of our approach in accurately classifying tweets into positive, negative, and neutral sentiment categories. The visualizations of class distribution, number of tokens per tweet, and word clouds provide further insights into the sentiment landscape on Twitter. Our research contributes to the development of sustainable and inclusive approaches for Twitter opinion mining, ensuring minimal environmental impact while capturing valuable sentimental information.

Implementation of the COVID-19 Health Protocol in Grab Bike Riders in Kupang City

Covid-19 is an infectious disease caused by SARS-CoV-2. The behaviour of Grab Bike riders who do not follow health protocols can increase the spread of the Covid-19 virus. The initial survey of Covid-19 cases conducted by researchers with 10 riders Grab Bike Kupang found that 7 out of 10 Grab Bike riders had tested positive for Covid-19. Based on researchers' observations in the field, it is still possible to find Grab Bike riders who have not fully implemented every health protocol. Good knowledge and attitude can encourage Grab Bike riders to take action in implementing the Covid-19 health protocol. This study aims to describe the knowledge, attitudes, and actions of Grab Bike riders in implementing the Covid-19 health protocol in Kupang City to prevent the spread of Covid-19. This type of research is quantitative research with a descriptive survey method. This research was conducted in the Grab Bike riders work area in Kupang City with a total sample of 62 people and using an accidental sampling technique. Based on the results of the study, it is known that the knowledge of Grab Bike riders is in the good category, namely 55 people (88.7%), the enough categories are 7 people (11.3%), and there are no people in the fewer categories (0.0%). The attitude of Grab Bike riders is in the positive category, namely 62 people (100.0%), and there are no people in the negative category (0.0%). The actions of Grab Bike riders are in the good category, namely 54 people (87.1%) and the less category are 8 people (12, 9%). It is hoped that Grab Bike riders will always follow the Covid-19 health protocol that has been established and seek out the most up-to-date information on measures to prevent the Covid-19 virus from spreading.

Sentiment Analysis of Emirati Dialect

Recently, extensive studies and research in the Arabic Natural Language Processing (ANLP) field have been conducted for text classification and sentiment analysis. Moreover, the number of studies that target Arabic dialects has also increased. In this research paper, we constructed the first manually annotated dataset of the Emirati dialect for the Instagram platform. The constructed dataset consisted of more than 70,000 comments, mostly written in the Emirati dialect. We annotated the comments in the dataset based on text polarity, dividing them into positive, negative, and neutral categories, and the number of annotated comments was 70,000. Moreover, the dataset was also annotated for the dialect type, categorized into the Emirati dialect, Arabic dialects, and MSA. Preprocessing and TF-IDF features extraction approaches were applied to the constructed Emirati dataset to prepare the dataset for the sentiment analysis experiment and improve its classification performance. The sentiment analysis experiment was carried out on both balanced and unbalanced datasets using several machine learning classifiers. The evaluation metrics of the sentiment analysis experiments were accuracy, recall, precision, and f-measure. The results reported that the best accuracy result was 80.80%, and it was achieved when the ensemble model was applied for the sentiment classification of the unbalanced dataset.

A Study on the Effectiveness of Rumor Control via Social Media Networks to Alleviate Public Panic About COVID-19.

The COVID-19 outbreak triggered a massive spread of unverified news on social media and has become a source of rumors. This paper studies the impact of a virtual rumor control center (RCC) on Weibo user behavior. The collected COVID-19 breaking news stories were divided into positive, negative, and neutral categories, while the moderating effect model was used to analyze the influence of anti-rumor on user behavior (forwarding, liking, and commenting). Our research found that rumor refuting does not directly affect user behavior but does have an indirect moderating effect. Rumor refuting has a profound impact on user forwarding behavior in cases of positive and negative news. Specifically, when the epidemic becomes more serious, the role of rumor refuting becomes critical, and vice versa. Refuting rumors reduces user willingness to forward positive or negative news, with more impact on negative news. Time lag analysis shows a significant moderation of unverified news within 72 h of refuting rumors but indicated an apparent weakening trend over time. Furthermore, we discovered non-linear feature and counter-cyclical phenomena in the moderating effect of rumor refutation.

Emotion schema effects on associative memory differ across emotion categories at the behavioural, physiological and neural level

Previous behavioural and neuroimaging studies have consistently reported that memory is enhanced for associations congruent or incongruent with the structure of prior knowledge, termed as schemas. However, it remains unclear if similar effects arise with emotion-related associations, and whether they depend on the type of emotions.Here, we addressed this question using a novel face-word pair association paradigm combined with fMRI and eye-tracking techniques. In two independent studies, we demonstrated and replicated that both congruency with emotion schemas and emotion category interact to affect associative memory. Overall, memory retrieval was higher for faces from pairs congruent vs. incongruent with emotion schemas, paralleled by a greater recruitment of left inferior frontal gyrus (IFG) during successful encoding. However, emotion schema effects differed across two negative emotion categories. Disgust was remembered better than fear, and only disgust activated left IFG stronger during encoding of congruent vs. incongruent pairs, suggestive of deeper semantic processing for the associations. On the contrary, encoding of congruent fear vs. disgust-related pairs was accompanied with greater activity in right fusiform gyrus (FG), suggesting a stronger sensory processing of faces. In addition, successful memory formation for congruent disgust pairs was associated with a higher pupil dilation index related to sympathetic activation, longer gaze time on words compared to faces, and more gaze switches between paired words and faces. This was reversed for fear-related congruent pairs where the faces attracted longer gaze time (compared to words).Overall, our results provide converging evidence from behavioural, physiological, and neural measures to suggest that congruency with available emotion schemas influence memory associations in a similar manner to semantic schemas. However, these effects vary across distinct emotion categories, pointing to a differential role of semantic processing and visual attention processes in the modulation of memory by disgust and fear, respectively.

Sentiment Analysis Using Product Based Reviews

Sentiment analysis is opinion mining in which it uses natural language processing and extracts the reviews in positive, negative and neutral categories. This helps users to identify the emotional tone behind the body of a text. Sentiment analysis computes the user opinion, attitudes towards the product, and the emotions to that product. Some machine learning techniques are used to identify the sentiment for the product. This model tests the reviews using various machine learning algorithms. Logistic regression algorithm has given the highest accuracy as compared to other algorithms.

A Corpus-Driven Study on the Revolutionary Image Construction of Sun Yat-sen in English Literature from the Perspective of Attitudinal System in Appraisal Theory

Under the background of developing the Guangdong-Hong Kong-Macau Greater Bay Area and with the purpose of responding to initiatives named “co-building humanities in bay area” and “supporting Zhongshan to promote Sun Yat-sen’s culture,” this research applies AntConc software to establish the English literature corpus related to Sun Yat-sen’s revolutionary deeds and analyzes the affect system, judgement system, and appreciation system in the English literature with the theoretical framework of the attitudinal system in appraisal theory. The following are discovered. (1) The proportion of the affect system in the corpus is the largest, while the proportion of the appreciation system is the smallest. Three systems differ greatly in quantity. (2) Focuses described by three systems are consistent. Through specific language representations, it is believed that Sun Yat-sen has the determination to dedicate and sacrifice for the revolution. He is undoubtedly the leader and pioneer of Chinese revolution. (3) Among 1854 language representations, positive attitudinal resources account for as much as 70%, which has important enlightenment in translating Sun Yat-sen’s revolutionary deeds to foreigners. Translators should reduce the usage of negative category of the attitudinal system as much as possible and adapt to the internal and ingrained cognitive model of western readers by increasing the usage of positive category of the attitudinal system. In this way, barriers from language representations and conflicts related to the deep culture will be eliminated, and ultimately Sun Yat-sen’s revolutionary culture will be exchanged and promoted at home and abroad.

«DARKNESS» AND «EMPTINESS» OF THE SUBJECT. COMPARATIVE ANALYSES OF «MYSTICAL THEOLOGY» AND «THE HEART SUTRA»

This article is the result of a compare analyses of the treatise by Pseudo- Dionysius the Areopagite «Mystical Theology» and one of the central sutras of the Mahayana’s tradition «The Heart Sutra». This article has the following objectives: firstly, to conduct a comparative analysis of two fundamental texts, in which displayed the central categories of each tradition. Special attention was paid to negative notions and also to concepts that maximally generalize and express denials. These primarily «the darkness» by Pseudo-Denys and «the emptiness» by the author of Heart Sutra. Secondly, to explain of the patterns identified during the research. According to the author, modern scientific studies devote low attention to the problem of interpretations of irrational categories. However, these categories are used in a rational discourse, often without hermeneutic strategies. An analyses of the modern thematic literatures to show the absence of a single consensus when addressing to issues related to the categorical apparatus of mystical-religious traditions.Their semantic content is often explained by the influence of the previous tradition, by the dominant culture and chronological relevance. According to the author opinion, there are fundamental similarities and background content of some categories, which are used in different traditions. Originality: Firstly, in the approach itself – the text considered in the article where not previously compared. Secondly, as a result of comparing the main categorical apparatus of the texts manifest consisted pattern of content, methods of presentation and structure of both treatises themselves and categories under consideration. Thirdly, the article offers an interpretation of the revealed patterns in the context of the consequence of the autodeanthropomorfization.The studied treatises have some coincidences are observed at the level of organization of the text, as well as pattern consisting in a consistent “unfolding” of a content from a central negative category, which, in turn is a category symbolizing the state of the subject outside of the self-creating experience.

Examining the Impact of Individualized Music for Patients on a Geriatric Psychiatry Unit

<h3>Introduction</h3> Clinicians are encouraged to use non-pharmacologic interventions first as part of the treatment of responsive behaviours due to mental health conditions. Music therapy is an example of such an intervention. In recent years, there has been an increased research focus on individualized music (IM) because it considers patient's personal music preferences. However, the findings of whether IM listening is more beneficial than general music listening has been inconsistent. This study is to further compare the effects of IM and elevator/baseline music (BM) listening on enjoyment behaviours of geriatric inpatients. <h3>Methods</h3> Fifteen participants were recruited from a geriatric psychiatry unit in an acute tertiary mental health facility in British Columbia, Canada. Their mean age was 74 years and their mean MMSE was 18. Each participant attended two 30-minute music listening sessions. Each session included a 15-minute baseline music playlist and a 15-minute IM playlist. The sequence of the two playlists was counterbalanced for each participant across the two sessions. The IM playlists were created by interviewing each participant with the Revised versions of the Assessment of Personalized Music Preference Questionnaire (APMPQ). The baseline (elevator) playlist was simply consisted of instrumental, non-lyrical jazz and classical music and was the same for every participant. The enjoyment behaviors during the sessions for each participant were measured by the Enjoyment Behavioral Coding Scheme (EBCS), which was developed for this project based on previous literature and was shown to have a good inter rater reliability. The EBCS was completed by the recreation therapist immediately after sessions. <h3>Results</h3> The average ratings of positive and negative items of the EBCS across two sessions of IM and BM were calculated. Paired samples t-test was used to compare the average ratings for the IM and BM sessions. For both the positive and negative behaviour categories, the t-tests of the average ratings did not show statistical significance. <h3>Conclusions</h3> Participants appeared to enjoy both the IM and baseline music sessions equally. <h3>This research was funded by</h3> NA

Identification of Extracapsular Extension in Head and Neck Cancer Patients Using Artificial Intelligence.

<h3>Purpose/Objective(s)</h3> Routine diagnostic CT scan guides the management of head and neck squamous cell carcinoma (HNSCC)) by identifying tumor and lymph node features. Nevertheless, lymph node extracapsular extension (ECE) remains challenging to detect by clinicians even using modern imaging techniques. It is essential to detect the occurrence of ECE as it changes staging and management for the patients. Current clinical ECE detection relies on clinical examination, radiological identification, and histopathological confirmation. Therefore, we aim to perform ECE identification automatically using a gradient-based class activation mapping (Grad-CAM) technique to guide the deep learning model to focus on the regions highly suspicious of ECE. The presence or absence of ECE has been analyzed and correlated with gold standard histopathological findings. <h3>Materials/Methods</h3> This research proposes a novel deep learning framework to detect ECE from 3D head and neck CT scans. The research problem is treated as a classification task, i.e., to label patients' CT scans with ECE positive or negative categories. Grad-CAM can provide visual explanations for deep neural networks by indicating the critical regions in the image for prediction. The proposed network has two convolutional neural networks (CNNs) involved. The first CNN model acts as a preprocessing step. The Grad-CAM method is applied to the trained CNN to highlight and extract the volumes of interest (VOIs) related to the class ECE positive. The second CNN model takes the extracted VOIs as model input for ECE positive/negative classification. The data used for the second CNN training does not involve that in the first step training process. The studied dataset includes 130 patients' CT scans. Cross-validation has been applied in our experimental tests. <h3>Results</h3> The experimental results have demonstrated that our model can identify ECE positive and negative patients based on ten-fold cross-validation. We have achieved 95.03% test accuracy and 90.90% test AUC in ECE classification. Furthermore, ECE probability heatmaps are generated by Grad-CAM. <h3>Conclusion</h3> This study demonstrates that a deep learning model guided by the Grad-CAM approach can be used for ECE detection. The proposed two-step neural networks can help capture sufficient features and achieve high classification accuracy. The heatmaps can be used as a tool for radiologists to assist in the identification of ECE. The outcome of this research is expected to promote the implementation of artificial intelligence for head and neck cancer ECE identification diagnosis in the radiology computer vision field.

STUDENT'S PERCEPTION ANALYSIS OF THE APPLICATION OF THE SELF-LEARNING LEARNING MODEL IN LEARNING

The purpose of the following research is to obtain student perceptions of the application of the Self-Regulated Learning learning model in class XI DPIB at SMKN 1 Kediri. Self-Regulated Learning in this study with three main factors according to Zimmerman, namely individual factors that have a relationship with student self-efficacy, behavioral factors that have a relationship with self-observation, self-reaction, and environmental factors. This type of quantitative descriptive research uses survey methods. The data collection technique was carried out using a questionnaire instrument. The data analysis technique was descriptive statistics using the percentage formula. The population in this study was 71 students through the use of the Proportional Random Sampling technique. Certainty of the number of tests, i.e. if the population is small, below 100, is considered a population study because the entire population is taken as a test-test. The findings of the study indicate that the perceptions of students in class XI DPIB are in the very positive category 0%, the positive category 50.70%, the medium category 23.94%, the negative category 18.31%, and the very negative category 7.04%. The positive results are given the conclusion that most of the students of class XI DPIB at SMK Negeri 1 Kediri have high Self-Regulated Learning and can self-regulate to achieve learning goals.

The Fear of Black Men? A Fanonian Cartography of Anti-Black Misandry as Psycho-Sexual Pathology

In contemporary discourse across disciplines in the social sciences and humanities, it is common to see descriptions and narratives about Black males framed in the negative categories of hypermasculinity, hypersexuality, hegemony, patriarchy, and misogyny (Oparanozie, Sales, DiClemente, & Braxton 2012; Smiley & Fakunle, 2016; Littles, 2020; Logan, 2020 and Moore, 2021). For instance, Moore (2021) describes Black males as individuals who demonstrate toxic behavioral traits such as aggression, patriarchal thoughts and dominance, and hegemonic masculinity. Similarly, Collins (2005) describes Black men as possessing the combination of hyper-sexuality and proclivity towards committing a crime which makes them inherently violent. Similarly, hooks (2004) describe Black males as dangerous because their pursuit of social and political power has led them to completely embrace patriarchal (hegemonic or toxic) masculinity. Black men in America have historically been depicted as violent and threatening (Hackman, 2013). All these negative ascriptions contribute to the general view of Black males as dangerous and socially deviant beings that deserves to be "feared." As such, many whites in the United States have grown up fearing Black male bodies, fraternizing about their sexuality as well as framing their natural corporeal movements as menacing. This is now even more apparent with the frequency with which many white members of law enforcement, as well as white vigilantes, are not legally held accountable for the frequent blatant murder of Black men and boys in America today, especially when "fear" is offered as the justification for such cold-blooded murderous acts. Thus, the Black male body is subjected to the white gaze as something to be feared, a site of terror and threat, which often results in violent forms of aggression, discrimination, and in extreme cases, death. In this paper, I use Fanon's notion of phobogenics and the Subordinate Male Target Hypothesis (SMTH) under Social Dominance Theory, as theoretical frameworks to expose the notion of "the fear of Black men" as an irrationally fabricated rhetorical trope. Unveiling this trope as part of the psychology of oppression deployed by dominant white males to maintain power and perpetuate both racialized and gendered discrimination against Black males. This paper concludes that "the fear of Black males" in America should be understood as the upshot of a form of antiblack misandry that specifically targets Black males and overdetermines their existence.

Negative cluster categories from simple minded collection quadruples

Fomin and Zelevinsky’s definition of cluster algebras laid the foundation for cluster theory. The various categorifications and generalizations of the original definition led to Iyama and Yoshino’s generalized cluster categories coming from positive-Calabi-Yau triples Jin later defined simple minded collection quadruples where the special case is the analogue of Iyama and Yang’s triples: negative-Calabi-Yau triples. In this paper, we further study the quotient categories coming from simple minded collection quadruples. Our main result uses limits and colimits to describe Hom-spaces over in relation to the easier to understand Hom-spaces over Moreover, we apply our theorem to give a different proof of a result by Jin: if we have a negative-Calabi-Yau triple, then is a negative cluster category.

Relationship Between Self-Esteem with the Ability of Emotional Managementi Victims of Bullying Teenager

Introduction: Victims of bullying teenager gets pressure that can lead to decrease selfesteem and emotional management skills. The aimed of this study is to know the relationship between self-esteem with the ability of emotional management in victims of bullying teenager at Sunan Giri junior high school Malang. Method: The research design used a correlational descriptive design with a cross sectional approach. The research population was 155 students in junior high school Sunan Giri Malang and 112 students was obtained by with simple Random Sampling. The independent variable in this study was self-esteem and dependent variable in this study was the ability to manage emotions. The data were collected by Rosenberg Self Esteem Scale and Emotion Regulation Questionnaire (ERQ) self-esteem Questionnaires and then analyzed by the Fisher's Exact test. Results: The results showed 69 (61.6%) respondents have low self-esteem and 65 (58.0%) respondents have negative category emotion management skills in victims of bullying teenajer. The results of the Fisher's Exact test showed there was a relationship between self esteem and the ability of emotional management in victims of bullying teenager in junior high school Sunan giri Malang (p = 0.000). Conclusions: There is a relationship between with self-esteem the ability of emotional management in victims of bullying teenager in Sunan giri junior high school Malang. For further research expected to examine other factors that cause self-esteem and emotional management skills such as physical conditions, parenting patterns and social classes.

BIOECOLOGY OF GIANT FRESHWATER PRAWNS (MACROBRACHIUM ROSENBERGII) IN THE BILAH RIVER, LABUHANBATU DISTRICT, NORTH SUMATERA

Giant freshwater prawns (Macrobrachium rossenbergii) were still been the favorite prawn catch of local fishermen in the Labuhanbatu district. This raises concerns for the survival of these animals. Therefore, it is necessary to conduct a study to obtain bioecological information on this giant freshwater, giant prawns in the Bilah river especially inform about condition the abundance based on gonad maturity levels, and the growth patterns of Giant freshwater prawns (M. rossenbergii). This study was conducted at 3 stations around the Bilah river. These stations were determined using a purposive sampling method. Furthermore, sampling was carried out using fishing nets. After that, the caught fish were analyzed to find out the Fulton’s condition factor, the abundance of prawns based on gonad maturity levels, and the growth patterns. The results of growth patterns analysis which calculate from the relationship of carapace length and body weight of M. rossenbergii caught were showed negative allometric category (b value 1.88 to 2.577). Meanwhile, the abundance of giant freshwater prawns based on gonad maturity levels for males and females were dominated by immature prawns (ranging from 46.15 to 54.65%).

Persepsi Peserta Didik Terhadap Pembelajaran Renang Pada Kelas X SMA Negeri 1 Pare Tahun Pelajaran 2019/2020

This study aims to determine how big the students' perception of swimming lessons in class X SMA N 1 Pare in the 2019/2020 school year. This research is a quantitative descriptive research with survey method. The population in this study were students of class X SMA N 1 Pare totaling 190 students. The sampling technique used proportional random sampling technique. Validation of the data using the product moment formula with rhit &gt; rtab (0.361) there are 28 valid questions. Data analysis was carried out by descriptive analysis. The research instrument uses a questionnaire. The results showed as many as 65 students or (50.39%) in the very positive category, as many as 61 students or (47.29%) in the positive category, as many as 3 students or (2.33%) in the less positive category and 0.00% in the negative category.

From coercion to respectful care: women\u2019s interactions with health care providers when planning a VBAC

BackgroundIn many countries caesarean section rates are increasing and this impacts on choices made around mode of birth in subsequent pregnancies. Having a vaginal birth after caesarean (VBAC) can be a safe and empowering experience for women, yet most women have repeat caesareans. High caesarean section rates increase maternal and neonatal morbidity, health costs and burden on hospitals. Women can experience varied support from health care providers when planning a VBAC. The aim of this paper is to explore the nature and impact of the interactions between women planning a VBAC and health care providers from the women’s perspective.MethodsA national Australian VBAC survey was undertaken in 2019. In total 559 women participated and provided 721 open-ended responses to six questions. Content analysis was used to categorise respondents’ answers to the open-ended questions.ResultsTwo main categories were found capturing the positive and negative interactions women had with health care providers. The first main category, ‘Someone in my corner’, included the sub-categories ‘belief in women birthing’, ‘supported my decisions’ and ‘respectful maternity care’. The negative main category ‘Fighting for my birthing rights’ included the sub-categories ‘the odds were against me’, ‘lack of belief in women giving birth’ and ‘coercion’. Negative interactions included the use of coercive comments such as threats and demeaning language. Positive interactions included showing support for VBAC and demonstrating respectful maternity care.ConclusionsIn this study women who planned a VBAC experienced a variety of positive and negative interactions. Individualised care and continuity of care are strategies that support the provision of positive respectful maternity care.

Komparasi Model Klasifikasi Sentimen Issue Vaksin Covid-19 Berbasis Platform Instagram

Information about COVID-19 or regulations regarding it has been massively shared by the Ministry of Health, via kemenkes_ri account. Including the topic of vaccinations. Although health research has been conducted to support the covid-19 vaccine campaign, however there are still people who give negative comments. Sentiment is also found in the kemenkes_ri account. Sentiment analysis is an opinion classification process to determine the responses given are included in the positive, negative or neutral categories. In this study, it is proposed to compare the performance of the SVM and KNN algorithms to classify sentiment in the kemenkes_ri account related to vaccine policy in Indonesia. Sentiment is classified into 3 polarities namely neutral, positive, and negative. The purpose of this comparison is to compare the best classification model on the topic of vaccine issue sentiment analysis, especially the Instagram platform. In this study, the stages started from the comment scrapping technique which resulted in 2,925 records. Preprocessing using NLP technique and weighting using TF-IDF technique. Next, the SMOTE technique was performed to avoid imbalancing class. The ratio of training and testing data is 9:1. The results showed that the best classification is SVM = 98.30%, while the KNN method is 80.03%.

ON THE CATEGORY OF NEGATION IN MEDIEVAL ENGLISH LITERATURE

The article “On the category of negation in Medieval English literature” deals with the main aspects of expressing the category of negation in Medieval English literature, based the example of the “Canterbury Tales” by Geoffrey Chaucer. Among the most productive ways we mention negative affixes, negative pronouns, negative adverbs, mononegation and polynegation at the syntactic level.