The Workshop on Computational Personality Recognition 2014

Celli, Fabio; Lepri, Bruno; Biel, Joan-Isaac; Gática-Pérez, Daniel; Riccardi, Giuseppe; Pianesi, Fabio

doi:10.1145/2647868.2647870

Cited by 97 publications

(108 citation statements)

References 7 publications

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“…and personality traits, both self-assessed [25], [26], [32], [27], [28], [29] and attributed [29], [30], [31]. Table I The column "Other" refers to works using models different from the Big-Five.…”

Section: B Personality and Social Mediamentioning

confidence: 99%

“…However, the performance seems to be higher for those traits where one of the three classes is more represented than the others, then the improvement is low with respect to a basic approach always giving as output the most represented class. APR on Facebook profiles was the subject of an international benchmarking campaign 3 the results of which appear in [32]. The main indication of this initiative is that selection techniques applied to large sets of initial features lead to the highest performances.…”

Section: B Personality and Social Mediamentioning

confidence: 99%

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The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits

Segalin

Perina

Cristani

et al. 2017

IEEE Trans. Affective Comput.

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Abstract-Flickr allows its users to tag the pictures they like as "favorite". As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-theart of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits).

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Section: B Personality and Social Mediamentioning

confidence: 99%

Section: B Personality and Social Mediamentioning

confidence: 99%

The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits

Segalin

Perina

Cristani

et al. 2017

IEEE Trans. Affective Comput.

View full text Add to dashboard Cite

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“…We consider both the final scalar outcome and the difference of each of the individual vector dimensions as features. Similarly to previous research (Mairesse et al, 2007;Celli et al, 2013), the bottom-up word based approach is outperformed by top-down semantic approaches which employ a more abstract feature representation. As in previous work, LIWC features exhibit good performance.…”

Section: Classification Approach For Direct Speechmentioning

confidence: 94%

“…More recent work in this area focuses on the personality prediction in social networks Kosinski et al, 2014) and multimodal personality prediction (Biel and Gatica-Perez, 2013;Aran and Gatica-Perez, 2013). These trends emphasized the correlation of network features and audiovisual features with extraversion, giving rise to the Workshop on Computational Personality Recognition (for an overview see (Celli et al, 2013;Celli et al, 2014).…”

Section: Related Workmentioning

confidence: 99%

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Flek

Gurevych

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.

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“…The datasets used for the training are taken from the Workshop on Computational Personality Recognition (WCPR) (Kosinski and Stillwell, 2012;Celli et al, 2014). The Facebook and Youtube personality datasets were combined and used for training.…”

Section: Personality Induction From Textmentioning

confidence: 99%

Proceedings of ACL 2017, System Demonstrations

Zhang

2017

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We present the first open-source tool for annotating morphosyntactic tense, mood and voice for English, French and German verbal complexes. The annotation is based on a set of language-specific rules, which are applied on dependency trees and leverage information about lemmas, morphological properties and POS-tags of the verbs. Our tool has an average accuracy of about 76%. The tense, mood and voice features are useful both as features in computational modeling and for corpuslinguistic research. IntroductionNatural language employs, among other devices such as temporal adverbials, tense and aspect to locate situations in time and to describe their temporal structure (Deo, 2012). The tool presented here addresses the automatic annotation of morphosyntactic tense, i.e., the tense-aspect combinations, expressed in the morphology and syntax of verbal complexes (VC). VCs are sequences of verbal tokens within a verbal phrase. We address German, French and English, in which the morphology and syntax also includes information on mood and voice. Morphosyntactic tenses do not always correspond to semantic tense (Deo, 2012). For example, the morphosyntactic tense of the English sentence "He is leaving at noon." is present progressive, while the semantic tense is future. In the remainder of this paper, we use the term tense to refer to the morphological tense and aspect information encoded in finite verbal complexes.Corpus-linguistic research, as well as automatic modeling of mono-and cross-lingual use of tense, mood and voice will strongly profit from a reliable automatic method for identifying these clausal features. They may, for instance, be used to classify texts with respect to the epoch or region in which they have been produced, or for assigning texts to a specific author. Moreover, in crosslingual research, tense, mood, and voice have been used to model the translation of tense between different language pairs (Santos, 2004; Loáiciga et al., 2014; Ramm and Fraser, 2016)). Identifying the morphosyntactic tense is also a necessary prerequisite for identifying the semantic tense in synthetic languages such as English, French or German (Reichart and Rappoport, 2010). The extracted tense-mood-voice (TMV) features may also be useful for training models in computational linguistics, e.g., for modeling of temporal relations (Costa and Branco, 2012; UzZaman et al., 2013).As illustrated by the examples in Figure 1, relevant information for determining TMV is given by syntactic dependencies and partially by partof-speech (POS) tags output by analyzers such as Mate (Bohnet and Nivre, 2012). However, the parser's output is not sufficient for determining TMV features; morphological features and lexical information needs to be taken into account as well. Learning TMV features from an annotated corpus would be an alternative; however, to the best of our knowledge, no such large-scale corpora exist.A sentence may contain more than one VC, and the tokens belonging to a VC are not always contiguous in the sentence (see VCs A...

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The Workshop on Computational Personality Recognition 2014

Cited by 97 publications

References 7 publications

The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits

The Pictures We Like Are Our Image: Continuous Mapping of Favorite Pictures into Self-Assessed and Attributed Personality Traits

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Proceedings of ACL 2017, System Demonstrations

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