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1
Exploiting Structure For Sentiment Classification
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2
Quantising Opinions for Political Tweets Analysis
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/217_Paper.pdf (2012)
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3
Hindi subjective lexicon: A lexical resource for hindi adjective polarity classification
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/673_Paper.pdf (2012)
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4
Hindi Subjective Lexicon: A Lexical Resource for Hindi Polarity Classification
In: http://web2py.iiit.ac.in/research_centres/publications/download/inproceedings.pdf.a92646aa66336f21.4c5245432731322d3637332e706466.pdf (2012)
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5
Learning Sentiment Lexicons in Spanish
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/1081_Paper.pdf (2012)
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6
Empatweet: Annotating and detecting emotions on Twitter
In: http://www.lrec-conf.org/proceedings/lrec2012/pdf/201_Paper.pdf (2012)
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7
Alleviating Data Sparsity for Twitter Sentiment Analysis
In: http://ceur-ws.org/Vol-838/paper_01.pdf (2012)
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8
Sur l'application de méthodes textométriques à la construction de critères de classification en analyse des sentiments
In: Actes de la conférence conjointe JEP-TALN-RECITAL 2012, volume 2: TALN ; TALN 2012 ; https://halshs.archives-ouvertes.fr/halshs-00702366 ; TALN 2012, Jun 2012, Grenoble, France. pp.367-374 (2012)
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9
Getting the word out: neural correlates of enthusiastic message propagation.
In: Frontiers in human neuroscience, vol 6, iss OCTOBER 2012 (2012)
Abstract: What happens in the mind of a person who first hears a potentially exciting idea?We examined the neural precursors of spreading ideas with enthusiasm, and dissected enthusiasm into component processes that can be identified through automated linguistic analysis, gestalt human ratings of combined linguistic and non-verbal cues, and points of convergence/divergence between the two. We combined tools from natural language processing (NLP) with data gathered using fMRI to link the neurocognitive mechanisms that are set in motion during initial exposure to ideas and subsequent behaviors of these message communicators outside of the scanner. Participants' neural activity was recorded as they reviewed ideas for potential television show pilots. Participants' language from video-taped interviews collected post-scan was transcribed and given to an automated linguistic sentiment analysis (SA) classifier, which returned ratings for evaluative language (evaluative vs. descriptive) and valence (positive vs. negative). Separately, human coders rated the enthusiasm with which participants transmitted each idea. More positive sentiment ratings by the automated classifier were associated with activation in neural regions including medial prefrontal cortex; MPFC, precuneus/posterior cingulate cortex; PC/PCC, and medial temporal lobe; MTL. More evaluative, positive, descriptions were associated exclusively with neural activity in temporal-parietal junction (TPJ). Finally, human ratings indicative of more enthusiastic sentiment were associated with activation across these regions (MPFC, PC/PCC, DMPFC, TPJ, and MTL) as well as in ventral striatum (VS), inferior parietal lobule and premotor cortex. Taken together, these data demonstrate novel links between neural activity during initial idea encoding and the enthusiasm with which the ideas are subsequently delivered. This research lays the groundwork to use machine learning and neuroimaging data to study word of mouth communication and the spread of ideas in both traditional and new media environments.
Keyword: Cognitive Sciences; Experimental Psychology; fMRI; information diffusion; natural language processing; Neurosciences; Psychology; sentiment analysis; word-of-mouth
URL: https://escholarship.org/uc/item/4j23v35s
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10
Automatic, adaptive, and applicative sentiment analysis ; Analyse de sentiments automatique, adaptative et applicative
Pak, Alexander. - : HAL CCSD, 2012
In: https://tel.archives-ouvertes.fr/tel-00717329 ; Other [cs.OH]. Université Paris Sud - Paris XI, 2012. English. ⟨NNT : 2012PA112101⟩ (2012)
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11
N.: Multi-class twitter emotion classification: A new approach
In: http://research.ijais.org/volume4/number1/ijais12-450651.pdf (2012)
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12
Approaches to Automatically Constructing Polarity Lexicons for Sentiment Analysis on Social Networks
In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1343187623 (2012)
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13
Linguistic Approach to Information Extraction and Sentiment Analysis on Twitter
In: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1342544962 (2012)
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14
Towards a Chinese common and common sense knowledge base for sentiment analysis
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15
Sur l'application de méthodes textométriques à la construction de critères de classification en analyse des sentiments
In: Actes de la conférence conjointe JEP-TALN-RECITAL 2012, volume 2: TALN ; TALN 2012 ; https://halshs.archives-ouvertes.fr/halshs-00702366 ; TALN 2012, Jun 2012, Grenoble, France. pp.367-374 (2012)
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16
Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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17
Extracting Diverse Sentiment Expressions with Target-Dependent Polarity from Twitter
In: Proceedings of the International AAAI Conference on Web and Social Media; Vol. 6 No. 1 (2012): Sixth International AAAI Conference on Weblogs and Social Media ; 2334-0770 ; 2162-3449 (2012)
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18
Cross-domain co-extraction of sentiment and topic lexicons
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19
Fine granular aspect analysis using latent structural models
Minlie Huang; Lei Fang. - : Association for Computational Linguistics, 2012
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20
A scaling approach for emotion-aware microblogging analysis: ...
Χατζάκου, Δέσποινα Δημητρίου. - : Aristotle University of Thessaloniki, 2012
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