Facial expressions alter the fundamental sound properties of speech

Literature from across academic disciplines has demonstrated significant links between emotional valence and language. For example, Whissell’s Dictionary of Affect in Language defines three dimensions upon which the emotionality of words is describable, and Ekman’s Theories of Emotion include the perception and internalization of facial expressions. The present study seeks to expand upon these works by exploring whether holding facial expressions alters the fundamental speech properties of spoken language. Nineteen (19) participants were seated in a soundproof chamber and were asked to speak a series of pseudowords containing target phonemes.  The participants spoke the pseudowords either holding no facial expression, smiling, or frowning, and the utterances recorded using a high-definition microphone and phonologically analysed using PRAAT analysis software. Analyses revealed a pervasive gender differences in frequency variables, where males showed lower fundamental but higher formant frequencies compared to females. Significant main effects were found within the fundamental and formant frequencies, but no effects were discerned for the intensity variable. While intricate, these results are indicative of an interaction between the activity of facial musculature when reflecting emotional valence and the sound properties of speech uttered simultaneously

Emerging Synergisms Between Drugs and Physiologically-Patterned Weak Magnetic Fields: Implications for Neuropharmacology and the Human Population in the Twenty-First Century

Synergisms between pharmacological agents and endogenous neurotransmitters are familiar and frequent. The present review describes the experimental evidence for interactions between neuropharmacological compounds and the classes of weak magnetic fields that might be encountered in our daily environments. Whereas drugs mediate their effects through specific spatial (molecular) structures, magnetic fields mediate their effects through specific temporal patterns. Very weak (microT range) physiologically-patterned magnetic fields synergistically interact with drugs to strongly potentiate effects that have classically involved opiate, cholinergic, dopaminergic, serotonergic, and nitric oxide pathways. The combinations of the appropriately patterned magnetic fields and specific drugs can evoke changes that are several times larger than those evoked by the drugs alone. These novel synergisms provide a challenge for a future within an electromagnetic, technological world. They may also reveal fundamental, common physical mechanisms by which magnetic fields and chemical reactions affect the organism from the level of fundamental particles to the entire living system

Knowledge of Greek and Latin Roots is Related to Reading Comprehension among French-Speaking Sixth Graders

By the end of primary school, students are confronted with expository texts known for their high proportion of domain-specific academic vocabulary words. These words usually comprise Greek or Latin roots in their internal structure. Recent findings showed that knowledge of Greek and Latin roots is related to reading comprehension. However, no study has investigated such a relationship in a francophone context. Therefore, the present study sought to measure Greek and Latin roots’ relation to reading comprehension among French 6th graders. To do so, 40 participants were administrated an experimental task on Greek and Latin roots knowledge and a reading comprehension standardized subset test. Variables related to reading comprehension, such as morphological awareness, vocabulary breadth, word reading fluency, oral comprehension, and working memory were also measured. Results showed that knowledge of Greek and Latin roots significantly predicted variation of reading comprehension. This paper discusses scientific and educational implications of this finding.À la fin de l'école primaire, les élèves sont confrontés à des textes explicatifs connus pour leur forte proportion de mots du vocabulaire académique spécifiques à un domaine. Ces mots comprennent généralement des racines grecques ou latines dans leur structure interne. Des découvertes récentes ont montré que la connaissance des racines grecques et latines est liée à la compréhension de la lecture. Cependant, aucune étude n'a investigué une telle relation dans un contexte francophone. Par conséquent, la présente étude a cherché à mesurer la relation entre les racines grecques et latines et la compréhension de la lecture chez les élèves francophones de 6e année du primaire. Pour ce faire, 40 participants ont été soumis à une tâche expérimentale sur la connaissance des racines grecques et latines et à un sous-test standardisé de compréhension de la lecture. Des variables liées à la compréhension de la lecture telles que la conscience morphologique, l'étendue du vocabulaire, la fluidité de la lecture des mots, la compréhension orale et la mémoire de travail ont également été mesurées. Les résultats ont montré que la connaissance des racines grecques et latines prédisait de manière significative la variation de la compréhension de la lecture. Cet article discute des implications scientifiques et éducatives de cette découverte

Significant Feature Clustering

In this thesis, we present a new clustering algorithm we call Significance Feature Clustering, which is designed to cluster text documents. Its central premise is the mapping of raw frequency count vectors to discrete-valued significance vectors which contain values of -1, 0, or 1. These values represent whether a word is significantly negative, neutral, or significantly positive, respectively. Initially, standard tf-idf vectors are computed from raw frequency vectors, then these tf-idf vectors are transformed to significance vectors using a parameter alpha, where alpha controls the mapping -1, 0, or 1 for each vector entry. SFC clusters agglomeratively, with each document's significance vector representing a cluster of size one containing just the document, and iteratively merges the two clusters that exhibit the most similar average using cosine similarity. We show that by using a good alpha value, the significance vectors produced by SFC provide an accurate indication of which words are significant to which documents, as well as the type of significance, and therefore correspondingly yield a good clustering in terms of a well-known definition of clustering quality. We further demonstrate that a user need not manually select an alpha as we develop a new definition of clustering quality that is highly correlated with text clustering quality. Our metric extends the family of metrics known as internal similarity, so that it can be applied to a tree of clusters rather than a set, but it also factors in an aspect of recall that was absent from previous internal similarity metrics. Using this new definition of internal similarity, which we call maximum tree internal similarity, we show that a close to optimal text clustering may be picked from any number of clusterings created by different alpha's. The automatically selected clusterings have qualities that are close to that of a well-known and powerful hierarchical clustering algorithm

Evaluating Clusterings by Estimating Clarity

In this thesis I examine clustering evaluation, with a subfocus on text clusterings specifically. The principal work of this thesis is the development, analysis, and testing of a new internal clustering quality measure called informativeness. I begin by reviewing clustering in general. I then review current clustering quality measures, accompanying this with an in-depth discussion of many of the important properties one needs to understand about such measures. This is followed by extensive document clustering experiments that show problems with standard clustering evaluation practices. I then develop informativeness, my new internal clustering quality measure for estimating the clarity of clusterings. I show that informativeness, which uses classification accuracy as a proxy for human assessment of clusterings, is both theoretically sensible and works empirically. I present a generalization of informativeness that leverages external clustering quality measures. I also show its use in a realistic application: email spam filtering. I show that informativeness can be used to select clusterings which lead to superior spam filters when few true labels are available. I conclude this thesis with a discussion of clustering evaluation in general, informativeness, and the directions I believe clustering evaluation research should take in the future

#Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection

[EN] Interest has grown around the classification of stance that users assume within online debates in recent years. Stance has been usually addressed by considering users posts in isolation, while social studies highlight that social communities may contribute to influence users¿ opinion. Furthermore, stance should be studied in a diachronic perspective, since it could help to shed light on users¿ opinion shift dynamics that can be recorded during the debate. We analyzed the political discussion in UK about the BREXIT referendum on Twitter, proposing a novel approach and annotation schema for stance detection, with the main aim of investigating the role of features related to social network community and diachronic stance evolution. Classification experiments show that such features provide very useful clues for detecting stance.The work of P. Rosso was partially funded by the Spanish MICINN under the research projects MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech(PGC2018-096212-B-C31) and PROMETEO/2019/121 (DeepPattern) of the Generalitat Valenciana. The work of V. Patti and G. Ruffo was partially funded by Progetto di Ateneo/CSP 2016 Immigrants, Hate and Prejudice in Social Media (S1618 L2 BOSC 01).Lai, M.; Patti, V.; Ruffo, G.; Rosso, P. (2020). #Brexit: Leave or Remain? The Role of User's Community and Diachronic Evolution on Stance Detection. Journal of Intelligent & Fuzzy Systems. 39(2):2341-2352. https://doi.org/10.3233/JIFS-179895S23412352392Blondel, V. D., Guillaume, J.-L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. doi:10.1088/1742-5468/2008/10/p10008Deitrick, W., & Hu, W. (2013). Mutually Enhancing Community Detection and Sentiment Analysis on Twitter Networks. Journal of Data Analysis and Information Processing, 01(03), 19-29. doi:10.4236/jdaip.2013.13004Duranti A. and Goodwin C. , Rethinking context: Language as an interactive phenomenon, Cambridge University Press, (1992).Evans A. , Stance and identity in Twitter hashtags, Language@ Internet 13(1) (2016).Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75-174. doi:10.1016/j.physrep.2009.11.002Gelman, A., & King, G. (1993). Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable? British Journal of Political Science, 23(4), 409-451. doi:10.1017/s0007123400006682Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling Users’ Activity on Twitter Networks: Validation of Dunbar’s Number. PLoS ONE, 6(8), e22656. doi:10.1371/journal.pone.0022656González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2008). Understanding individual human mobility patterns. Nature, 453(7196), 779-782. doi:10.1038/nature06958Hernández-Castañeda, Á., Calvo, H., & Gambino, O. J. (2018). Impact of polarity in deception detection. Journal of Intelligent & Fuzzy Systems, 35(1), 549-558. doi:10.3233/jifs-169610Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., … Van Alstyne, M. (2009). Computational Social Science. Science, 323(5915), 721-723. doi:10.1126/science.1167742Mohammad, S. M., Sobhani, P., & Kiritchenko, S. (2017). Stance and Sentiment in Tweets. ACM Transactions on Internet Technology, 17(3), 1-23. doi:10.1145/3003433Mohammad, S. M., & Turney, P. D. (2012). CROWDSOURCING A WORD-EMOTION ASSOCIATION LEXICON. Computational Intelligence, 29(3), 436-465. doi:10.1111/j.1467-8640.2012.00460.xPang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends® in Information Retrieval, 2(1–2), 1-135. doi:10.1561/1500000011Pennebaker J.W. , Francis M.E. and Booth R.J. , Linguistic Inquiry and Word Count: LIWC 2001, Mahway: Lawrence Erlbaum Associates 71 (2001).Sulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Theocharis, Y., & Lowe, W. (2015). Does Facebook increase political participation? Evidence from a field experiment. Information, Communication & Society, 19(10), 1465-1486. doi:10.1080/1369118x.2015.1119871Whissell, C. (2009). Using the Revised Dictionary of Affect in Language to Quantify the Emotional Undertones of Samples of Natural Language. Psychological Reports, 105(2), 509-521. doi:10.2466/pr0.105.2.509-52

Applying basic features from sentiment analysis on automatic irony detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19390-8_38People use social media to express their opinions. Often linguistic devices such as irony are used. From the sentiment analysis perspective such utterances represent a challenge being a polarity reversor (usually from positive to negative). This paper presents an approach to address irony detection from a machine learning perspective. Our model considers structural features as well as, for the first time, sentiment analysis features such as the overall sentiment of a tweet and a score of its polarity. The approach has been evaluated over a set classifiers such as: Naïve Bayes, Decision Tree, Maximum Entropy, Support Vector Machine, and for the first time in irony detection task: Multilayer Perceptron. The results obtained showed the ability of our model to distinguish between potentially ironic and non-ironic sentences.The National Council for Science and Technology (CONACyT Mexico) has funded the research work of the first author (Grant No.218109/313683, CVU-369616). The research work of third author was carried out inthe framework of WIQ-EI IRSES (Grant No. 269180) within the FP 7 Marie Curie, DIANA-APPLICATIONS (TIN2012-38603-C02-01) projects and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Hernández Farías, I.; Benedí Ruiz, JM.; Rosso, P. (2015). Applying basic features from sentiment analysis on automatic irony detection. En Pattern Recognition and Image Analysis: 7th Iberian Conference, IbPRIA 2015, Santiago de Compostela, Spain, June 17-19, 2015, Proceedings. Springer International Publishing. 337-344. https://doi.org/10.1007/978-3-319-19390-8_38S337344Alba-Juez, L.: Irony and the other off record strategies within politeness theory. J. Engl. Am. Stud. 16, 13–24 (1995)Attardo, S.: Irony markers and functions: towards a goal-oriented theory of irony and its processing. Rask 12, 3–20 (2000)Barbieri, F., Saggion, H.: Modelling Irony in Twitter, pp. 56–64. Association for Computational Linguistics (2014)Bosco, C., Patti, V., Bolioli, A.: Developing corpora for sentiment analysis: the case of irony and senti-tut. IEEE Intell. Syst. 28(2), 55–63 (2013)Buschmeier, K., Cimiano, P., Klinger, R.: An impact analysis of features in a classification approach to irony detection in product reviews. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 42–49. Association for Computational Linguistics (2014)Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Reyes, A., Barnden, J.: Sentiment analysis of figurative language in twitter. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2015), Co-located with NAACL and *SEM (2015)Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2004, pp. 168–177(2004)Maynard, D., Greenwood, M.: Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC-2014), European Language Resources Association (ELRA) (2014)Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet::similarity: measuring the relatedness of concepts. In: Proceedings of the 9th National Conference on Artificial Intelligence, pp. 1024–1025. Association for Computational LinguisticsReyes, A., Rosso, P., Veale, T.: A multidimensional approach for detecting irony in twitter. Lang. Resour. Eval. 47(1), 239–268 (2013)Wallace, B.C.: Computational irony: a survey and new perspectives. Artif. Intell. Rev. 43, 467–483 (2013)Wang, A.P.: #irony or #sarcasm – a quantitative and qualitative study based on twitter. In: Proceedings of the PACLIC: the 27th Pacific Asia Conference on Language, Information, and Computation, pp. 349–356. Department of English, National Chengchi University (2013)Whissell, C.: Using the revised dictionary of affect in language to quantify the emotional undertones of samples of natural languages. Psychol. Rep. 2, 509–521 (2009)Wolf, A.: Emotional expression online: gender differences in emoticon use. CyberPsychology Behavior 3, 827–833 (2000

Stratégies et outils d’aide à la rédaction d’un article scientifique empirique

Scientific writing is a laborious task for student researchers. The multiple knowledge and skills required throughout a process made up of several stages, namely planning, writing and revision (Hayes & Flower, 1980), make many people feel powerless when faced with this task. when they experience it for the first time. This article aims to provide them with strategies, tools, and tips for each of these steps when writing an empirical article. The authors share resources discovered or developed within the framework of their writing experiences.La rédaction scientifique est une tâche laborieuse pour les étudiants chercheurs. Les multiples connaissances et habiletés requises tout au long d’un processus composé de plusieurs étapes, à savoir la planification, la mise en texte et la révision (Hayes et Flower, 1980), font en sorte que plusieurs se sentent démunis face à cette tâche lorsqu’ils la vivent pour la première fois. Le présent article souhaite leur fournir des stratégies, des outils et des conseils pour chacune de ces étapes lors de la rédaction d’un article empirique. Les auteures y partagent des ressources découvertes ou développées dans le cadre de leurs expériences rédactionnelles