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To POS Tag or Not to POS Tag: The Impact of POS Tags on Morphological Learning in Low-Resource Settings ...
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Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models ...
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RNN Classification of English Vowels: Nasalized or Not
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In: Proceedings of the Society for Computation in Linguistics (2019)
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The focal alteration and causal connectivity in children with new-onset benign epilepsy with centrotemporal spikes
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Studies on the Differences Between Chinese and Western Nature Poems
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In: Studies in Literature and Language; Vol 10, No 3 (2015): Studies in Literature and Language; 83-88 ; 1923-1563 ; 1923-1555 (2015)
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Prediction of age, sentiment, and connectivity from social media text
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Abstract:
Social media corpora, including the textual output of blogs, forums, and messaging applications, provide fertile ground for linguistic analysis material diverse in topic and style, and at Web scale. We investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and author mood, of a large corpus of blog posts, to analyze the impact of age, emotion, and social connectivity. These properties are found to be significantly different across the examined cohorts, which suggest discriminative features for a number of useful classification tasks. We build binary classifiers for old versus young bloggers, social versus solo bloggers, and happy versus sad posts with high performance. Analysis of discriminative features shows that age turns upon choice of topic, whereas sentiment orientation is evidenced by linguistic style. Good prediction is achieved for social connectivity using topic and linguistic features, leaving tagged mood a modest role in all classifications.
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Keyword:
binary classifiers; bloggers; classification tasks; discriminative features; linguistic analysis; linguistic features; social media
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URL: http://hdl.handle.net/10536/DRO/DU:30044670
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