Letteratura scientifica selezionata sul tema "Sentient Machine"
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Articoli di riviste sul tema "Sentient Machine"
Bronfman, Zohar, Simona Ginsburg e Eva Jablonka. "When Will Robots Be Sentient?" Journal of Artificial Intelligence and Consciousness 08, n. 02 (6 agosto 2021): 183–203. http://dx.doi.org/10.1142/s2705078521500168.
Testo completoCastro, Paulo. "Lying, computers and self-awareness". Kairos. Journal of Philosophy & Science 24, n. 1 (1 dicembre 2020): 10–34. http://dx.doi.org/10.2478/kjps-2020-0009.
Testo completoTellols, Dolça, Maite Lopez-Sanchez, Inmaculada Rodríguez, Pablo Almajano e Anna Puig. "Enhancing sentient embodied conversational agents with machine learning". Pattern Recognition Letters 129 (gennaio 2020): 317–23. http://dx.doi.org/10.1016/j.patrec.2019.11.035.
Testo completoBiever, Celeste. "9 Will we ever build a sentient machine?" New Scientist 206, n. 2754 (marzo 2010): 32–33. http://dx.doi.org/10.1016/s0262-4079(10)60801-9.
Testo completoTorrens, Paul M. "Smart and Sentient Retail High Streets". Smart Cities 5, n. 4 (29 novembre 2022): 1670–720. http://dx.doi.org/10.3390/smartcities5040085.
Testo completoSmith, G. W. "Art, Aliens and the Machine". Leonardo 51, n. 5 (ottobre 2018): 551–52. http://dx.doi.org/10.1162/leon_a_01222.
Testo completoSmith, Dale. "The Trouble with Sentient Beings, and: The Time Machine Paradox". Colorado Review 37, n. 3 (2010): 144–46. http://dx.doi.org/10.1353/col.2010.0037.
Testo completoJocz, Artur. "If Machines Want to Dream... Adam Wiśniewski-Snerg on Ethical Consequences of There Being No Substantial Distinction between Humans and Robots". ETHICS IN PROGRESS 10, n. 2 (10 ottobre 2019): 45–51. http://dx.doi.org/10.14746/eip.2019.2.5.
Testo completoPATRA, INDRAJIT. "The Battle Within and the Battle Without: The Posthuman Worldview of Ken MacLeod’s The Corporation Wars Trilogy". American, British and Canadian Studies 37, n. 1 (1 dicembre 2021): 85–104. http://dx.doi.org/10.2478/abcsj-2021-0019.
Testo completoWilson, Daniel C. S., Mariona Coll Ardanuy, Kaspar Beelen, Barbara McGillivray e Ruth Ahnert. "The Living Machine: A Computational Approach to the Nineteenth-Century Language of Technology". Technology and Culture 64, n. 3 (luglio 2023): 875–902. http://dx.doi.org/10.1353/tech.2023.a903976.
Testo completoTesi sul tema "Sentient Machine"
OGURI, PEDRO. "MACHINE LEARNING FOR SENTIMENT CLASSIFICATION". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9947@1.
Testo completoSentiment Analysis é um problema de categorização de texto no qual deseja-se identificar opiniões favoráveis e desfavoráveis com relação a um tópico. Um exemplo destes tópicos de interesse são organizações e seus produtos. Neste problema, documentos são classificados pelo sentimento, conotação, atitudes e opiniões ao invés de se restringir aos fatos descritos neste. O principal desafio em Sentiment Classification é identificar como sentimentos são expressados em textos e se tais sentimentos indicam uma opinião positiva (favorável) ou negativa (desfavorável) com relação a um tópico. Devido ao crescente volume de dados disponível na Web, onde todos tendem a ser geradores de conteúdo e expressarem opiniões sobre os mais variados assuntos, técnicas de Aprendizado de Máquina vem se tornando cada vez mais atraentes. Nesta dissertação investigamos métodos de Aprendizado de Máquina para Sentiment Analysis. Apresentamos alguns modelos de representação de documentos como saco de palavras e N-grama. Testamos os classificadores SVM (Máquina de Vetores Suporte) e Naive Bayes com diferentes modelos de representação textual e comparamos seus desempenhos.
Sentiment Analysis is a text categorization problem in which we want to identify favorable and unfavorable opinions towards a given topic. Examples of such topics are organizations and its products. In this problem, docu- ments are classifed according to their sentiment, connotation, attitudes and opinions instead of being limited to the facts described in it. The main challenge in Sentiment Classification is identifying how sentiments are expressed in texts and whether they indicate a positive (favorable) or negative (unfavorable) opinion towards a topic. Due to the growing volume of information available online in an environment where we all tend to be content generators and express opinions on a variety of subjects, Machine Learning techniques have become more and more attractive. In this dissertation, we investigate Machine Learning methods applied to Sentiment Analysis. We present document representation models such as bag-of-words and N-grams.We compare the performance of the Naive Bayes and the Support Vector Machine classifiers for each proposed model
Alotaibi, Saud Saleh. "Sentiment analysis in the Arabic language using machine learning". Thesis, Colorado State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3720340.
Testo completoSentiment analysis has recently become one of the growing areas of research related to natural language processing and machine learning. Much opinion and sentiment about specific topics are available online, which allows several parties such as customers, companies and even governments, to explore these opinions. The first task is to classify the text in terms of whether or not it expresses opinion or factual information. Polarity classification is the second task, which distinguishes between polarities (positive, negative or neutral) that sentences may carry. The analysis of natural language text for the identification of subjectivity and sentiment has been well studied in terms of the English language. Conversely, the work that has been carried out in terms of Arabic remains in its infancy; thus, more cooperation is required between research communities in order for them to offer a mature sentiment analysis system for Arabic. There are recognized challenges in this field; some of which are inherited from the nature of the Arabic language itself, while others are derived from the scarcity of tools and sources.
This dissertation provides the rationale behind the current work and proposed methods to enhance the performance of sentiment analysis in the Arabic language. The first step is to increase the resources that help in the analysis process; the most important part of this task is to have annotated sentiment corpora. Several free corpora are available for the English language, but these resources are still limited in other languages, such as Arabic. This dissertation describes the work undertaken by the author to enrich sentiment analysis in Arabic by building a new Arabic Sentiment Corpus. The data is labeled not only with two polarities (positive and negative), but the neutral sentiment is also used during the annotation process.
The second step includes the proposal of features that may capture sentiment orientation in the Arabic language, as well as using different machine learning classifiers that may be able to work better and capture the non-linearity with a richly morphological and highly inflectional language, such as Arabic. Different types of features are proposed. These proposed features try to capture different aspects and characteristics of Arabic. Morphological, Semantic, Stylistic features are proposed and investigated. In regard with the classifier, the performance of using linear and nonlinear machine learning approaches was compared. The results are promising for the continued use of nonlinear ML classifiers for this task. Learning knowledge from a particular dataset domain and applying it to a different domain is one useful method in the case of limited resources, such as with the Arabic language. This dissertation shows and discussed the possibility of applying cross-domain in the field of Arabic sentiment analysis. It also indicates the feasibility of using different mechanisms of the cross-domain method.
Other work in this dissertation includes the exploration of the effect of negation in Arabic subjectivity and polarity classification. The negation word lists were devised to help in this and other natural language processing tasks. These words include both types of Arabic, Modern Standard and some of Dialects. Two methods of dealing with the negation in sentiment analysis in Arabic were proposed. The first method is based on a static approach that assumes that each sentence containing negation words is considered a negated sentence. When determining the effect of negation, different techniques were proposed, using different word window sizes, or using base phrase chunk. The second approach depends on a dynamic method that needs an annotated negation dataset in order to build a model that can determine whether or not the sentence is negated by the negation words and to establish the effect of the negation on the sentence. The results achieved by adding negation to Arabic sentiment analysis were promising and indicate that the negation has an effect on this task. Finally, the experiments and evaluations that were conducted in this dissertation encourage the researchers to continue in this direction of research.
Paknejad, Sepideh. "Sentiment classification on Amazon reviews using machine learning approaches". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233551.
Testo completoEftersom marknadsplatser online har varit populära under de senaste decennierna, så har online-säljare och inköpsmän ställt kunderna frågor om deras åsikter gällande varorna de har köpt. Som ett resultat genereras miljontals recensioner dagligen vilket gör det svårt för en potentiell konsument att fatta ett bra beslut om de ska köpa produkten eller inte. Att analysera den enorma mängden åsikter är också svårt och tidskrävande för produktproducenter. Denna avhandling tar upp problemet med att klassificera recensioner med deras övergripande semantiska (positiva eller negativa). För att genomföra studien har två olika övervakade maskininlärningstekniker, SVM och Naïve Bayes, testats på recensioner av skönhetsprodukter från Amazon. Deras noggrannhet har sedan jämförts. Resultaten visade att SVM-tillvägagångssättet överträffar Naïve Bayes-tillvägagångssättet när datasetet är större. Båda algoritmerna nådde emellertid lovande noggrannheter på minst 80%.
WESTLING, ANDERS. "Sentiment Analysisof Microblog Posts from a Crisis Eventusing Machine Learning". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138428.
Testo completoI och med att tjänster för sociala medier blir allt mer populära, existerar det nu en konstant ström av åsikter fritt tillgängliga på internet. Dessa åsikter kan analyseras för att finna användarnas känslor kring olika ämnen. Ett exempel av intresse är att se hur folk känner under en krissituation för att få en bättre uppfattning om vilken typ av hjälp som skulle vara till mest nytta för tillfället. Målet med detta examensarbete har varit att se om det är möjligt att skapa en automatisk klassificerare, baserad på maskininlärningsmetoder, som med precision kan avgöra huruvida ett mikroblogginlägg skrivet under en politisk händelse i Ryssland är för, emot, eller neutral till den grupp människor som händelsen kretsar kring. Problemet väntas vara svårare än sentimentanalys av normallånga texter, detta eftersom mikroblogginlägg är mycket kortare och ofta har ett informellt språk. Ett antal olika algoritmer för maskininlärning studerades tillsammans med olika metoder för att representera mikroblogginläggen på ett format som algoritmerna kan arbeta med. De mest lovande utav dessa algoritmer och representationer implementerades och testades för att se om en effektiv klassificerare kunde åstakommas. Resultaten visar att algoritmerna inte är tillräckligt bra för att skapa en tillräckligt precis klassificerare med den träningsdata som användes. En stor faktor tros vara den lilla mängden träningsdata som användes. En bättre klassificerare skulle potentiellt kunna uppnås om genom att använda fler mikrobloginlägg som träningsdata. Det vore även intressant att utforska andra sentimentklassificeringar utav mikroblogginlägg, då den som användes i det här arbetet tros vara särskilt svår. Den här studien och tidigare forskning på liknande klassificeringar talar för att detta är ett svårt problem som kräver mer arbete för att en precis klassificerare ska kunna erhållas.
Erogul, Umut. "Sentiment Analysis In Turkish". Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610616/index.pdf.
Testo completoDi, Gennaro Pierluigi. "Due approcci alla sentiment polarity classification di tweet per la lingua italiana". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13270/.
Testo completoVaswani, Vishwas. "Predicting sentiment-mention associations in product reviews". Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/13714.
Testo completoDepartment of Computing and Information Sciences
Doina Caragea
With the rising trend in social networking, more people express their opinions on the web. As a consequence, there has been an increase in the number of blogs where people write reviews about the products they buy or services they experience. These reviews can be very helpful to other potential customers who want to know the pros and cons of a product, and also to manufacturers who want to get feedback from customers about their products. Sentiment analysis of online data (such as review blogs) is a rapidly growing field of research in Machine Learning, which can leverage online reviews and quickly extract the sentiment of a whole blog. The accuracy of a sentiment analyzer relies heavily on correctly identifying associations between a sentiment (opinion) word and the targeted mention (token or object) in blog sentences. In this work, we focus on the task of automatically identifying sentiment-mention associations, in other words, we identify the target mention that is associated with a sentiment word in a sentence. Support Vector Machines (SVM), a supervised machine learning algorithm, was used to learn classifiers for this task. Syntactic and semantic features extracted from sentences were used as input to the SVM algorithm. The dataset used in the work has reviews from car and camera domain. The work is divided into two phases. In the first phase, we learned domain specific classifiers for the car and camera domains, respectively. To further improve the predictions of the domain specific classifiers we investigated the use of transfer learning techniques in the second phase. More precisely, the goal was to use knowledge from a source domain to improve predictions for a target domain. We considered two transfer learning approaches: a feature level fusion approach and a classifier level fusion approach. Experimental results show that transfer learning can help to improve the predictions made using the domain specific classifier approach. While both the feature level and classifier level fusion approaches were shown to improve the prediction accuracy, the classifier level fusion approach gave better results.
Svensson, Kristoffer. "Sentiment Analysis With Convolutional Neural Networks : Classifying sentiment in Swedish reviews". Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-64768.
Testo completoCAMBA, GIACOMO. "Machine Learning in Social Media Sentiment Classification and Trading Strategy Design". Doctoral thesis, Università degli Studi di Cagliari, 2022. http://hdl.handle.net/11584/333407.
Testo completoCAPUA, M. DI. "A DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS". Doctoral thesis, Università degli Studi di Milano, 2017. http://hdl.handle.net/2434/467844.
Testo completoSentiment Analysis refers to the process of computationally identifying and categorizing opinions expressed in a piece of text, in order to determine whether the writer’s attitude towards a particular topic or product is positive, negative, or even neutral. The views expressed and its related concepts, such as feelings, judgments, and emotions have become recently a subject of study and research in both academic and industrial areas. Unfortunately language comprehension of user comments, especially in social networks, is inherently complex to computers. The ways in which humans express themselves with natural language are nearly unlimited and informal texts is riddled with typos, misspellings, badly set up syntactic constructions and also specific symbols (e.g. hashtags in Twitter) which exponentially complicate this task. Recently, deep learning approaches are emerging as powerful computational models that discover intricate semantic representations of texts automatically from data without hand-made feature engineering. These approaches have improved the state-of-the-art in many Sentiment Analysis tasks including sentiment classification of sentences or documents, sentiment lexicon learning and also in more complex problems as cyber bullying detection. The contributions of this work are twofold. First, related to the general Sentiment Analysis problem, we propose a semi-supervised neural network model, based on Deep Belief Networks, able to deal with data uncertainty for text sentences in Italian language. We test this model against some datasets from literature related to movie reviews, adopting a vectorized representation of text (Word2Vec) and exploiting methods from Natural Language Processing (NLP) pre-processing. Second, assuming that the cyber bullying phenomenon can be treated as a particular Sentiment Analysis problem, we propose an unsupervised approach to automatic cyber bullying detection in social networks, based both on Growing Hierarchical Self Organizing Map (GHSOM) and on a new specific features model, showing that our solution can achieve interesting results, respect to classical supervised approaches.
Libri sul tema "Sentient Machine"
Affective computing and sentiment analysis: Emotion, metaphor and terminology. Dordrecht: Springer, 2011.
Cerca il testo completoHusain, Amir. Sentient Machine: The Coming Age of Artificial Intelligence. Scribner, 2017.
Cerca il testo completoThe sentient machine: The coming age of artificial intelligence. Scribner, 2017.
Cerca il testo completoThe Sentient Machine: The Coming Age of Artificial Intelligence. Scribner, 2018.
Cerca il testo completoGILCHRIST, Alasdair. Thinking Machines : Book I Robotics: From Mechanical to Sentient Machines. Independently Published, 2017.
Cerca il testo completoGrimm, Joshua. Ex Machina. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781800348301.001.0001.
Testo completoGoodman, Adam. The Deportation Machine. Princeton University Press, 2020. http://dx.doi.org/10.23943/princeton/9780691182155.001.0001.
Testo completoPapacharissi, Zizi. Networked Self and Human Augmentics, Artificial Intelligence, Sentience. Taylor & Francis Group, 2018.
Cerca il testo completoPapacharissi, Zizi. Networked Self and Human Augmentics, Artificial Intelligence, Sentience. Taylor & Francis Group, 2018.
Cerca il testo completoPapacharissi, Zizi. Networked Self and Human Augmentics, Artificial Intelligence, Sentience. Taylor & Francis Group, 2018.
Cerca il testo completoCapitoli di libri sul tema "Sentient Machine"
Moreno-Ortiz, Antonio. "Sentiment". In Making Sense of Large Social Media Corpora, 141–68. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52719-7_6.
Testo completoCerulli, Giovanni. "Sentiment Analysis". In Fundamentals of Supervised Machine Learning, 365–84. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41337-7_8.
Testo completoRöchert, Daniel, German Neubaum e Stefan Stieglitz. "Identifying Political Sentiments on YouTube: A Systematic Comparison Regarding the Accuracy of Recurrent Neural Network and Machine Learning Models". In Disinformation in Open Online Media, 107–21. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61841-4_8.
Testo completoSarkar, Dipanjan, Raghav Bali e Tushar Sharma. "Analyzing Movie Reviews Sentiment". In Practical Machine Learning with Python, 331–72. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3207-1_7.
Testo completoAggarwal, Charu C. "Opinion Mining and Sentiment Analysis". In Machine Learning for Text, 413–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73531-3_13.
Testo completoAggarwal, Charu C. "Opinion Mining and Sentiment Analysis". In Machine Learning for Text, 491–514. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96623-2_15.
Testo completoMahata, Sainik Kumar, Anupam Mondal, Monalisa Dey e Darothi Sarkar. "Sentiment Analysis using Machine Translation". In Applications of Machine Intelligence in Engineering, 371–77. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003269793-40.
Testo completoDenecke, Kerstin. "Machine Learning-Based Sentiment Analysis Approaches". In Sentiment Analysis in the Medical Domain, 71–78. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30187-2_11.
Testo completoGerard, Charlie. "Text classification and sentiment analysis". In Practical Machine Learning in JavaScript, 67–134. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6418-8_4.
Testo completoLi, Qingyuan, Kai Zhang, Lin Sun e Ruichen Xia. "Detecting Negative Sentiment on Sarcastic Tweets for Sentiment Analysis". In Artificial Neural Networks and Machine Learning – ICANN 2023, 479–91. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44204-9_40.
Testo completoAtti di convegni sul tema "Sentient Machine"
Beesley, Philip, Asya Zeliha Ilgun, Giselle Bouron, David Kadish, Jordan Prosser, Rob Gorbet, Dana Kulic, Paul Nicholas e Mateusz Zwierzycki. "Hybrid Sentient Canopy: An implementation and visualization of proprioreceptive curiosity-based machine learning". In ACADIA 2016: Post-Human Frontiers. ACADIA, 2016. http://dx.doi.org/10.52842/conf.acadia.2016.362.
Testo completoR. Hodeghatta, Umesh, e Sanath V. Haritsa. "Covid-19 Twitter Sentiments Across the United States in August 2020". In International Conference on AI, Machine Learning and Applications (AIMLA 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111305.
Testo completoLa Russa, Federico Mario, e Cettina Santagati. "From the Cognitive to the Sentient Building - Machine Learning for the preservation of museum collections in historical architecture". In eCAADe 2020: Anthropologic : Architecture and Fabrication in the cognitive age. eCAADe, 2020. http://dx.doi.org/10.52842/conf.ecaade.2020.2.507.
Testo completoBhatt, Saachin, Mustansar Ghazanfar e Mohammad Hossein Amirhosseini. "Machine Learning based Cryptocurrency Price Prediction using Historical Data and Social Media Sentiment". In 5th International Conference on Machine Learning & Applications. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131001.
Testo completoThapa, Bipun. "Sentiment Analysis of Cyber Security Content on Twitter and Reddit". In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120708.
Testo completoJalalahmadi, Behrooz, John Slotwinski, Jingfu Liu, Jason Rios, Christopher Peitsch, Arnold Goldberg e Timothy Montalbano. "In-process Defect Monitoring and Correction in Additive Manufacturing of Aluminum Alloys". In Vertical Flight Society 75th Annual Forum & Technology Display. The Vertical Flight Society, 2019. http://dx.doi.org/10.4050/f-0075-2019-14623.
Testo completoWright, Rewa, e Simon Howden. "Nga manawataki o te koiora: biological rhythms, posthuman design and decolonial thought". In 28th International Symposium on Electronic Art. Paris: Ecole des arts decoratifs - PSL, 2024. http://dx.doi.org/10.69564/isea2023-35-short-wright-et-al-nga-manawataki-o-te-koiora.
Testo completoS. Chu, Jason, e Sindhu Ghanta. "Integrative Sentiment Analysis: Leveraging Audio, Visual, and Textual Data". In 4th International Conference on AI, Machine Learning and Applications. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.140211.
Testo completoChen, Zhijun. "High-Frequency Cryptocurrency Trading Strategy using Tweet Sentiment Analysis". In 2nd International Conference on Machine Learning Techniques and NLP (MLNLP 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.111410.
Testo completoArora, Adwita, Krish Chopra, Divya Chaudhary, Ian Gorton e Bijendra Kumar. "Sentiment Analysis of Social Media Data on COVID-19". In 4th International Conference on Natural Language Processing and Machine Learning. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130802.
Testo completoRapporti di organizzazioni sul tema "Sentient Machine"
Hua, Tianyu. Machine learning for sentiment analysis: Opportunities and challenges. Ames (Iowa): Iowa State University, maggio 2022. http://dx.doi.org/10.31274/cc-20240624-974.
Testo completoLasko, Kristofer, e Elena Sava. Semi-automated land cover mapping using an ensemble of support vector machines with moderate resolution imagery integrated into a custom decision support tool. Engineer Research and Development Center (U.S.), novembre 2021. http://dx.doi.org/10.21079/11681/42402.
Testo completoMuñoz-Martínez, Jonathan Alexander, David Orozco e Mario A. Ramos-Veloza. Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia. Banco de la República, novembre 2023. http://dx.doi.org/10.32468/be.1256.
Testo completoBecker, Sarah, Megan Maloney e Andrew Griffin. A multi-biome study of tree cover detection using the Forest Cover Index. Engineer Research and Development Center (U.S.), settembre 2021. http://dx.doi.org/10.21079/11681/42003.
Testo completoAdegoke, Damilola, Natasha Chilambo, Adeoti Dipeolu, Ibrahim Machina, Ade Obafemi-Olopade e Dolapo Yusuf. Public discourses and Engagement on Governance of Covid-19 in Ekiti State, Nigeria. African Leadership Center, King's College London, dicembre 2021. http://dx.doi.org/10.47697/lab.202101.
Testo completo