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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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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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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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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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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.
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Keyword:
computer science; Lexicon Generation; Machine learning; Sentiment Analysis; Twitter
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URL: https://hdl.handle.net/10289/9567 https://doi.org/10.1145/2766462.2767770
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