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Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English
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In: Front Artif Intell (2020)
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Hybrid Hashtags: #YouKnowYoureAKiwiWhen Your Tweet Contains Māori and English
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10 |
Māori loanwords: a corpus of New Zealand English tweets
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In: Vocab@Leuven 2019 (2019)
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WASSA-2017 shared task on emotion intensity
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In: WASSA 2017 (2017)
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15 |
Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis
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Determining word–emotion associations from tweets by multi-label classification
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In: WI'16 (2016)
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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.
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Keyword:
computer science; Machine learning
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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
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In: WI'16 (2016)
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Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis
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In: 22nd European Conference on Artificial Intelligence (ECAI) (2016)
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From unlabelled tweets to Twitter-specific opinion words
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In: SIGIR '15 (2015)
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