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DWUG ES: Diachronic Word Usage Graphs for Spanish ...
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DWUG ES: Diachronic Word Usage Graphs for Spanish ...
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DWUG ES: Diachronic Word Usage Graphs for Spanish ...
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DWUG ES: Diachronic Word Usage Graphs for Spanish ...
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DWUG ES: Diachronic Word Usage Graphs for Spanish ...
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DWUG ES: Diachronic Word Usage Graphs for Spanish ...
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7
Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English
In: Front Artif Intell (2020)
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Contextualised approaches to embedding word senses
Ansell, Alan John. - : The University of Waikato, 2020
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Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English
Trye, David; Calude, Andreea S.; Bravo-Marquez, Felipe. - : Frontiers Media SA, 2020
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10
Māori loanwords: a corpus of New Zealand English tweets
In: Vocab@Leuven 2019 (2019)
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11
Transferring sentiment knowledge between words and tweets
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Emotion Intensities in Tweets ...
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WASSA-2017 shared task on emotion intensity
In: WASSA 2017 (2017)
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14
Emotion intensities in Tweets
In: *SEM 2017 (2017)
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Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis
Bravo-Marquez, Felipe. - : University of Waikato, 2017
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16
Determining word–emotion associations from tweets by multi-label classification
In: WI'16 (2016)
Abstract: The automatic detection of emotions in Twitter posts is a challenging task due to the informal nature of the language used in this platform. In this paper, we propose a methodology for expanding the NRC word-emotion association lexicon for the language used in Twitter. We perform this expansion using multi-label classification of words and compare different wordlevel features extracted from unlabelled tweets such as unigrams, Brown clusters, POS tags, and word2vec embeddings. The results show that the expanded lexicon achieves major improvements over the original lexicon when classifying tweets into emotional categories. In contrast to previous work, our methodology does not depend on tweets annotated with emotional hashtags, thus enabling the identification of emotional words from any domainspecific collection using unlabelled tweets.
Keyword: computer science; Machine learning
URL: https://hdl.handle.net/10289/10783
https://doi.org/10.1109/WI.2016.90
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17
Building a Twitter opinion lexicon from automatically-annotated tweets
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From opinion lexicons to sentiment classification of tweets and vice versa: a transfer learning approach
In: WI'16 (2016)
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19
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis
In: 22nd European Conference on Artificial Intelligence (ECAI) (2016)
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20
From unlabelled tweets to Twitter-specific opinion words
In: SIGIR '15 (2015)
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