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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 3 4 5 Hits 61 – 80 of 88 61 Evidence for causal top-down frontal contributions to predictive processes in speech perception Cope, Thomas E.; Sohoglu, E.; Sedley, W.. - : Nature Publishing Group UK, 2017 BASE Show details 62 Transcranial electric stimulation for the investigation of speech perception and comprehension Zoefel, Benedikt; Davis, Matthew H.. - : Routledge, 2017 BASE Show details 63 Machine Learning for Identifying Emotional Expression in Text: Improving the Accuracy of Established Methods Bantum, Erin O.; Elhadad, Noémie; Owen, Jason E.; Zhang, Shaodian; Golant, Mitch; Buzaglo, Joanne; Stephen, Joanne; Giese-Davis, Janine In: J Technol Behav Sci (2017) Abstract: Expression of emotion has been linked to numerous critical and beneficial aspects of human functioning. Accurately capturing emotional expression in text grows in relevance as people continue to spend more time in an online environment. The Linguistic Inquiry and Word Count (LIWC) is a commonly used program for the identification of many constructs, including emotional expression. In an earlier study (Bantum & Owen, 2009) LIWC was demonstrated to have good sensitivity yet poor positive predictive value. The goal of the current study was to create an automated machine learning technique to mimic manual coding. The sample included online support groups, cancer discussion boards, and transcripts from an expressive writing study, which resulted in 39,367 sentence-level coding decisions. In examining the entire sample the machine learning approach outperformed LIWC, in all categories outside of Sensitivity for negative emotion (LIWC Sensitivity = .85; Machine Learning Sensitivity = .41), although LIWC does not take into consideration prosocial emotion, such as affection, interest, and validation. LIWC performed significantly better than the machine learning approach when removing the prosocial emotions (p = <.0001). The sample over-represented examples of emotion that fit into the overarching category of positive emotion. Remaining work is needed to create more effective machine learning features for codes that are thought to be important emotionally but were not well represented in the sample (e.g., frustration, contempt, and belligerence), and Machine Learning could be a fruitful method for continued exploration. Keyword: Article URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7467127/ https://doi.org/10.1007/s41347-017-0015-5 BASE Hide details 64 Alcohol paper materials Sanghyuk Park (3745924); Clintin Davis-Stober (3208233); Daniel Cavagnaro (3745978). - 2017 BASE Show details 65 Bayes Factor table Sanghyuk Park (3745924); Clintin Davis-Stober (3208233); Daniel Cavagnaro (3745978). - 2017 BASE Show details 66 Recovery of orthographic processing after stroke: A longitudinal fMRI study Purcell, Jeremy; Sebastian, Rajani; Leigh, Richard. - 2017 BASE Show details 67 Key Factors in Obstetric Delivery Decision-Making among Asian and Pacific Islander Women by English Proficiency Davis, Chevelle MA; Guo, Mary; Miyamura, Jill. - : University Clinical, Education & Research Associate (UCERA), 2017 BASE Show details 68 Comparing and Validating Methods of Reading Instruction Using Behavioural and Neural Findings in an Artificial Orthography Taylor, J. S. H.; Davis, Matthew H.; Rastle, Kathleen. - : American Psychological Association, 2017 BASE Show details 69 Diverse Families’ Experiences with HPV Vaccine Information Sources: A Community-Based Participatory Approach1 Lai, Djin; Bodson, Julia; Davis, France A.. - 2017 BASE Show details 70 Learning Can Be Fun and Games Driver, Christina; Davis, Cindy. - : University of the Sunshine Coast, 2017 BASE Show details 71 The effect of spectral profile on the intelligibility of emotional speech in noise Davis, Chris (R11605); Chong, Cheeseng (R16836); Kim, Jeesun (R11607). - : France, International Speech Communication Association, 2017 BASE Show details 72 Older and younger adults' identification of sentences filtered with amplitude and frequency modulations in quiet and noise Mahajan, Yatin (R17503); Kim, Jeesun (R11607); Davis, Chris (R11605). - : U.S., Acoustical Society of America, 2017 BASE Show details 73 The influence of auditory-visual speech and clear speech on cross-language perceptual assimilation Fenwick, Sarah E. (S29421); Best, Catherine T. (R11322); Davis, Chris (R11605). - : Netherlands, Elsevier, 2017 BASE Show details 74 Translation of Markovnikov’s Magistr Khimii Dissertation: A Progress Report Davis, Alexander R.; Lewis, David E.. - 2017 BASE Show details 75 Evidence for causal top-down frontal contributions to predictive processes in speech perception Dawson, C; Wiggins, J; Griffiths, T D. - : Nature Publishing Group, 2017 BASE Show details 76 Using deep neural networks to estimate tongue movements from speech face motion Kroos, Christian; Bundgaard-Nielsen, Rikke L. (R14172); Best, Catherine T. (R11322). - : Sweden, KTH Royal Institute of Technology, 2017 BASE Show details 77 Indigenous Ways of Explaining Health and Illness Davis, Morgan; NC DOCKS at East Carolina University. - 2017 BASE Show details 78 Competitive civilizing missions: Hungarian Germans, modernization, and ethnographic descriptions of the Zigeuner before World War I Davis, Sacha E.. - : Cambridge University Press, 2017 BASE Show details 79 Evidence for causal top-down frontal contributions to predictive processes in speech perception Sohoglu E; Cope TE; Jones PS... In: Nature Communications, 1 December 2017 (2017) BASE Show details 80 The IELTS roller coaster: stories of hope, stress, success and despair Yucel, Megan; Iwashita, Noriko. - : Springer, 2017 BASE Show details Page: 1 2 3 4 5

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