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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5 Hits 1 – 20 of 87 1 Parler comme jamais. La langue, ce qu'on croit et ce qu'on en sait Véron, Laélia; Candea, Maria. - : HAL CCSD, 2021. : Le Robert / Binge audio, 2021 In: https://hal.archives-ouvertes.fr/hal-03377078 ; Le Robert / Binge audio, 2021, 9782321016687 (2021) BASE Show details 2 The Challenges of Digital Diplomacy in the Era of Globalization: The Case of the United Arab Emirates Antwi-Boateng, Osman; Al Mazrouei, Khadija Ali Mohammed In: International Journal of Communication; Vol 15 (2021); 19 ; 1932-8036 (2021) BASE Show details 3 Exploiting BERT and RoBERTa to Improve Performance for Aspect Based Sentiment Analysis Narayanaswamy, Gagan Reddy. - : Technological University Dublin, 2021 In: Dissertations (2021) BASE Show details 4 Distinguishing fake and real news of twitter data with the help of machine learning techniques Shah, Aanan. - : Laurentian University of Sudbury, 2021 Abstract: News articles have an influence on people's belief and views about various circumstances. In this regard, some news publishers with political or ideological bias try to spread news which are distorted or totally wrong. This thesis intends to develop a machine learning model that identifies fake news and original news by taking aid from natural language processing. Natural language processing was used to preprocess the text. Some general features like, number of words, sentences, stopwords, non-alphabetic words, verbs, nouns, and adjectives were identified. The stopwords and hyperlinks were removed to clean the text data. In the preprocessing step after cleaning the data and removing the stopwords, the position of each word was concatenated with the word itself. This procedure helps in distinguishing between a word as a noun, a pronoun, an adjective or a verb in the sentences. After preprocessing, feature extraction methods were used for converting the text of news to analyzable data. The frequency of the words in each article was used for filtering out the non-informative words. Three feature extraction methods were used in this study namely, count vectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and word2vec embedding. It was observed that the results obtained by TF-IDF feature extraction method were superior compared with the other two methods. After feature extraction, various machine learning models were used for training the model namely, Naive Bayes, Logistic Regression, Random Forest, K-nearest neighbors (KNN) and Support Vector Machine (SVM). The Recurrent Neural Network (RNN) was also used as a deep learning model. The model was successfully tested on two datasets. On the first dataset, SVM achieved an accuracy of 98.5% and RNN achieved an accuracy of 98.03% which is much improvement over the best results of Agarwalla et al., 2019 (83.16 % accuracy). On the second dataset, SVM achieved an accuracy of 97.76%, RNN achieved 97.1% and Logistic Regression achieved 97.50% which is an improvement over the best results of Vijayraghavan et al. 2020 (94.88% accuracy). ; MSc Computational Sciences Keyword: data; fake news; machine learning techniques; real news; twitter URL: https://zone.biblio.laurentian.ca/handle/10219/3847 BASE Hide details 5 Using language models for holistic language variety comparisons ... McNeill, Joshua. - : Open Science Framework, 2021 BASE Show details 6 Orthographic variation of (lol) on Twitter ... McNeill, Joshua. - : Open Science Framework, 2021 BASE Show details 7 Tweeting on dementia: A snapshot of the content and sentiment of tweets associated with dementia Robertshaw, David; Babicova, Ivana In: First Monday; Volume 26, Number 6 - 7 June 2021 ; 1396-0466 (2021) BASE Show details 8 Narrativised simile and emotional responses to Brexit Barbara Dancygier In: Russian Journal of Linguistics, Vol 25, Iss 3, Pp 663-684 (2021) (2021) BASE Show details 9 “You’re too thick to change the station” – Impoliteness, insults and responses to insults on Twitter Bączkowska Anna In: Topics in Linguistics, Vol 22, Iss 2, Pp 62-84 (2021) (2021) BASE Show details 10 Implicit vs explicit evaluation: How English-speaking Twitter users discuss migration problems Elena V. Gabrielova; Olga I. Maksimenko In: Russian Journal of Linguistics, Vol 25, Iss 1, Pp 105-124 (2021) (2021) BASE Show details 11 eterna regularidad de los participios Miguel Silvestre Llamas In: Domínios de Lingu@gem, Vol 16, Iss 2, Pp 843-869 (2021) (2021) BASE Show details 12 Spanish politicians in Twitter: A linguistic analysis of their written discourse Ricardo Casañ Pitarch In: Ibérica, Vol 40, Iss 1, Pp 195-216 (2021) (2021) BASE Show details 13 Construcción de la imagen colectiva de grupos a favor del Acuerdo de paz de Colombia en Twitter / Construction of the collective image of groups in favor of the Colombian peace agreement on Twitter Laura Cristina Bonilla-Neira In: Revista de Estudos da Linguagem, Vol 29, Iss 4, Pp 2225-2257 (2021) (2021) BASE Show details 14 Monitoring Users’ Behavior: Anti-Immigration Speech Detection on Twitter Pitropakis, Nikolaos; Kokot, Kamil; Gkatzia, Dimitra. - : MDPI, 2020 BASE Show details 15 Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19 ... Boukouvalas, Zois; Mallinson, Christine; Crothers, Evan. - : Maryland Shared Open Access Repository, 2020 BASE Show details 16 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 17 Temporally-Informed Analysis of Named Entity Recognition ... Rijhwani, Shruti; Preoțiuc-Pietro, Daniel. - : Zenodo, 2020 BASE Show details 18 Mapping the languages of Twitter in Finland: richness and diversity in space and time ... Hiippala, Tuomo; Väisänen, Tuomas; Toivonen, Tuuli. - : Zenodo, 2020 BASE Show details 19 Mapping the languages of Twitter in Finland: richness and diversity in space and time ... Hiippala, Tuomo; Väisänen, Tuomas; Toivonen, Tuuli. - : Zenodo, 2020 BASE Show details 20 A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration ... Banda, Juan M.; Tekumalla, Ramya; Wang, Guanyu. - : Zenodo, 2020 BASE Show details Page: 1 2 3 4 5

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