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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 Hits 1 – 20 of 23 1 DWUG ES: Diachronic Word Usage Graphs for Spanish ... Zamora-Reina, Frank D.; Bravo-Marquez, Felipe; Schlechtweg, Dominik. - : Zenodo, 2022 BASE Show details 2 DWUG ES: Diachronic Word Usage Graphs for Spanish ... Zamora-Reina, Frank D.; Bravo-Marquez, Felipe; Schlechtweg, Dominik. - : Zenodo, 2022 BASE Show details 3 DWUG ES: Diachronic Word Usage Graphs for Spanish ... Zamora-Reina, Frank D.; Bravo-Marquez, Felipe; Schlechtweg, Dominik. - : Zenodo, 2022 BASE Show details 4 DWUG ES: Diachronic Word Usage Graphs for Spanish ... Zamora-Reina, Frank D.; Bravo-Marquez, Felipe; Schlechtweg, Dominik. - : Zenodo, 2022 BASE Show details 5 DWUG ES: Diachronic Word Usage Graphs for Spanish ... Zamora-Reina, Frank D.; Bravo-Marquez, Felipe; Schlechtweg, Dominik. - : Zenodo, 2022 BASE Show details 6 DWUG ES: Diachronic Word Usage Graphs for Spanish ... Zamora-Reina, Frank D.; Bravo-Marquez, Felipe; Schlechtweg, Dominik. - : Zenodo, 2022 BASE Show details 7 Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English Trye, David; Calude, Andreea S.; Bravo-Marquez, Felipe... In: Front Artif Intell (2020) BASE Show details 8 Contextualised approaches to embedding word senses Ansell, Alan John. - : The University of Waikato, 2020 BASE Show details 9 Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English Trye, David; Calude, Andreea S.; Bravo-Marquez, Felipe. - : Frontiers Media SA, 2020 BASE Show details 10 Māori loanwords: a corpus of New Zealand English tweets Trye, David; Calude, Andreea S.; Bravo-Marquez, Felipe... In: Vocab@Leuven 2019 (2019) BASE Show details 11 Transferring sentiment knowledge between words and tweets Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard. - : IOS Press, 2018 BASE Show details 12 Emotion Intensities in Tweets ... Mohammad, Saif M.; Bravo-Marquez, Felipe. - : arXiv, 2017 BASE Show details 13 WASSA-2017 shared task on emotion intensity Bravo-Marquez, Felipe; Mohmmad, Saif M. In: WASSA 2017 (2017) BASE Show details 14 Emotion intensities in Tweets Mohammad, Saif M.; Bravo-Marquez, Felipe In: *SEM 2017 (2017) BASE Show details 15 Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis Bravo-Marquez, Felipe. - : University of Waikato, 2017 BASE Show details 16 Determining word–emotion associations from tweets by multi-label classification Bravo-Marquez, Felipe; Frank, Eibe; Mohammad, Saif M.... In: WI'16 (2016) BASE Show details 17 Building a Twitter opinion lexicon from automatically-annotated tweets Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard. - : Elsevier, 2016 BASE Show details 18 From opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approach Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard In: WI'16 (2016) BASE Show details 19 Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard In: 22nd European Conference on Artificial Intelligence (ECAI) (2016) BASE Show details 20 From unlabelled tweets to Twitter-specific opinion words Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard In: SIGIR '15 (2015) Abstract: In this article, we propose a word-level classification model for automatically generating a Twitter-specific opinion lexicon from a corpus of unlabelled tweets. The tweets from the corpus are represented by two vectors: a bag-of-words vector and a semantic vector based on word-clusters. We propose a distributional representation for words by treating them as the centroids of the tweet vectors in which they appear. The lexicon generation is conducted by training a word-level classifier using these centroids to form the instance space and a seed lexicon to label the training instances. Experimental results show that the two types of tweet vectors complement each other in a statistically significant manner and that our generated lexicon produces significant improvements for tweet-level polarity classification. Keyword: computer science; Lexicon Generation; Machine learning; Sentiment Analysis; Twitter URL: https://hdl.handle.net/10289/9567 https://doi.org/10.1145/2766462.2767770 BASE Hide details Page: 1 2

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