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dc.contributor.authorStolarski , Łukasz
dc.date.accessioned2023-01-10T06:36:22Z
dc.date.available2023-01-10T06:36:22Z
dc.date.issued2022-12-29
dc.identifier.issn1731-7533
dc.identifier.urihttp://hdl.handle.net/11089/45252
dc.description.abstractThe major aim of this paper is to establish possible correlations between continuous sentiment scores and four basic acoustic characteristics of voice. In order to achieve this objective, the text of “A Christmas Carol” by Charles Dickens was tokenized at the sentence level. Next, each of the resulting text units was assessed in terms of sentiment polarity and aligned with the corresponding fragment in an audiobook. The results indicate weak but statistically significant correlations between sentiment scores and three acoustic features: the mean F0, the standard deviation of F0 and the mean intensity. These findings may be useful in selecting optimal acoustic features for model training in multimodal sentiment analysis. Also, they are essential from a linguistic point of view and could be applied in studies on such language phenomena as irony.en
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesResearch in Language;2en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectsentiment analysisen
dc.subjectacoustic featuresen
dc.subjectfeature selectionen
dc.titleCorrelations Between Positive or Negative Utterances and Basic Acoustic Features of Voice: a Preliminary Analysisen
dc.typeArticle
dc.page.number153-178
dc.contributor.authorAffiliationJan Kochanowski University in Kielce, Polanden
dc.referencesAbbasi, Ahmed, Hassan, Ammar and Dhar, Milan.2014. Benchmarking Twitter Sentiment Analysis Tools. In LREC, Vol. 14, 26–31.en
dc.referencesAldeneh, Zakaria, Khorram, Soheil, Dimitriadis, Dimitrios and Provost, Emily Mower. 2017. Pooling acoustic and lexical features for the prediction of valence. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, 68–72. ACM. https://doi.org/10.1145/3136755.3136760en
dc.referencesAudacity Team. 2014. Audacity(R): Free audio editor and recorder (version 2.0.5) [computer software].en
dc.referencesAue, Anthony and Gamon, Michael. 2005. Customizing sentiment classifiers to new domains: A case study. In Proceedings of recent advances in natural language processing (RANLP), Vol. 1, 2–10. Citeseer.en
dc.referencesAustin, John Langshaw. 1962. How to do things with words. Oxford: Clarendon Press.en
dc.referencesBezooijen, Renée. 1984. Characteristics and recognizability of vocal expressions of emotion. Dordrecht, Netherlands: Foris Publications. https://doi.org/10.1515/9783110850390en
dc.referencesBoersma, Paul and Weenink, David. 2014. Praat, a system for doing phonetics by computer (version 5.4.01) [computer software]. Amsterdam: University of Amsterdam.en
dc.referencesBorth, Damian, Ji, Rongrong, Chen, Tao, Breuel, Thomas and Chang, Shih-Fu. 2013. Large-scale visual sentiment ontology and detectors using adjective noun pairs. In Proceedings of the 21st ACM international conference on Multimedia, 223–232. ACM. https://doi.org/10.1145/2502081.2502282en
dc.referencesBreitenstein, Caterina, Van Lancker, Diana and Daum, Irene. 2001. The contribution of speech rate and pitch variation to the perception of vocal emotions in a German and an American sample. Cognition and Emotion, 15(1), 57–79. https://doi.org/10.1080/02699930126095en
dc.referencesCambria, Erik, Poria, Soujanya, Bajpai, Rajiv and Schuller, Björn W. 2016. SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives. In COLING, 2666–2677.en
dc.referencesChen, Minghai, Wang, Sen, Liang, Paul Pu, Baltrušaitis, Tadas, Zadeh, Amir and Morency, Louis-Philippe. 2017. Multimodal sentiment analysis with word-level fusion and reinforcement learning. In Proceedings of the 19th ACM International Conference on Multimodal Interaction, 63–171. ACM. https://doi.org/10.1145/3136755.3136801en
dc.referencesChoi, Yejin and Cardie, Claire. 2008. Learning with compositional semantics as structural inference for subsentential sentiment analysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 793–801). Association for Computational Linguistics. https://doi.org/10.3115/1613715.1613816en
dc.referencesColeman, Robert F. and Williams, Robert. 1979. Identification of emotional states using perceptual and acoustic analyses. In Transcript of the 8th Symposium: Care of the Professional Voice, Part I. The Voice Foundation, New York.en
dc.referencesCollier, William G. and Hubbard, Timothy L. 1998. Judgments of happiness, brightness, speed, and tempo change of auditory stimuli varying in pitch and tempo. Psychomusicology, 17(1/2), 36–55. https://doi.org/10.1037/h0094060en
dc.referencesCollier, William G. and Hubbard, Timothy L. 2001. Musical scales and evaluations of happiness and awkwardness: Effects of pitch, direction, and scale mode. American Journal of Psychology, 114(3), 355–375. https://doi.org/10.2307/1423686en
dc.referencesDavitz, Joel R. 1964. Auditory correlates of vocal expressions of emotional meaning. The Communication of Emotional Meaning, 101–112.en
dc.referencesDiniz, Joao P., Bastos, Lucas, Soares, Elias, Ferreira, Miller, Ribeiro, Filipe and Benevenuto, Fabrıcio. 2016. ifeel 2.0: A multilingual benchmarking system for sentence-level sentiment analysis.en
dc.referencesEldred, Stanley H. and Price, Douglas B. 1958. A linguistic evaluation of feeling states in psychotherapy. Psychiatry, 21(2), 115–121. https://doi.org/10.1080/00332747.1958.11023120en
dc.referencesEyben, Florian, Weninger, Felix, Gross, Florian and Schuller, Björn. 2013. Recent developments in opensmile, the munich open-source multimedia feature extractor. In Proceedings of the 21st ACM international conference on Multimedia, 835–838. ACM. https://doi.org/10.1145/2502081.2502224en
dc.referencesFairbanks, Grant and Pronovost, Wilbert. 1939. An experimental study of the pitch characteristics of the voice during the expression of emotion. Speech Monographs, 6(1), 87–104. https://doi.org/10.1080/03637753909374863en
dc.referencesFonagy, Ivan.1978. A new method of investigating the perception of prosodic features. Language and Speech, 21(1), 34–49. https://doi.org/10.1177/002383097802100102en
dc.referencesFrick, Robert W. 1985. Communicating emotion: The role of prosodic features. Psychological Bulletin, 97(3), 412–429. https://doi.org/10.1037/0033-2909.97.3.412en
dc.referencesGoldman, Jean-Philippe. 2011. EasyAlign: an automatic phonetic alignment tool under Praat. In Proceedings of Interspeech, 3233–3236. https://doi.org/10.21437/Interspeech.2011-815en
dc.referencesGonçalves, Pollyanna, Araújo, Matheus, Benevenuto, Fabrício and Cha, Meeyoung. 2013. Comparing and combining sentiment analysis methods. In Proceedings of the first ACM conference on Online social networks (pp. 27–38). ACM. https://doi.org/10.1145/2512938.2512951en
dc.referencesGovindaraj, Sureshkumar and Gopalakrishnan, Kumaravelan. 2016. Intensified sentiment analysis of customer product reviews using acoustic and textual features. ETRI Journal, 38(3), 494–501. https://doi.org/10.4218/etrij.16.0115.0684en
dc.referencesHargreaves, William A., Starkweather, John A. and Blacker, K. H. 1965. Voice quality in depression. Journal of Abnormal Psychology, 70(3), 218–220. https://doi.org/10.1037/h0022151en
dc.referencesHöffe, Wilhelm L. 1960. Über Beziehungen von Sprachmelodie und Lautstärke. Phonetica, 5(3–4), 129–159. https://doi.org/10.1159/000258054en
dc.referencesHuddleston, Rodney. 1988. English grammar: An outline. Cambridge University Press. https://doi.org/10.1017/CBO9781139166003en
dc.referencesHuron, David. 2008. A comparison of average pitch height and interval size in major-and minor-key themes: Evidence consistent with affect-related pitch prosody. Empirical Musicology Review, 3(2), 59–63. https://doi.org/10.18061/1811/31940en
dc.referencesHuron, David, Yim, Gary, and Chordia, Parag. 2010. The effect of pitch exposure on sadness judgments: An association between sadness and lower than normal pitch. In Proceedings of the 11th International Conference on Music Perception and Cognition, 63–66.en
dc.referencesHuttar, George L. 1968. Relations between prosodic variables and emotions in normal American English utterances. Journal of Speech, Language, and Hearing Research, 11(3), 481–487. https://doi.org/10.1044/jshr.1103.481en
dc.referencesHutto, C. J. and Gilbert, Eric. 2014. Vader: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In Eighth International AAAI Conference on Weblogs and Social Media (ICWSM-14), 216–255. Ann Arbor, MI. https://doi.org/10.1609/icwsm.v8i1.14550en
dc.referencesKaiser, L. 1962. Communication of affects by single vowels. Synthese, 14(4), 300–319. https://doi.org/10.1007/BF00869311en
dc.referencesKappas, Arvid, Hess, Ursula and Scherer, Klaus R. 1991. Voice and emotion. In R. S. Feldman and B. Rim (eds.), Fundamentals of nonverbal behavior (pp. 200–238). Paris, France: Editions de la Maison des Sciences de l’Homme.en
dc.referencesKennedy, Alistair and Inkpen, Diana. 2005. Sentiment classification of movie and product reviews using contextual valence shifters. In Proceedings of the Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations. Ottawa, Ontario, Canada.en
dc.referencesKennedy, Alistair and Inkpen, Diana. 2006. Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence, 22(2), 110–125. https://doi.org/10.1111/j.1467-8640.2006.00277.xen
dc.referencesKiritchenko, Svetlana and Mohammad, Saif M. 2016a. Happy Accident: A Sentiment Composition Lexicon for Opposing Polarity Phrases. In Proceedings of the 10th edition of the Language Resources and Evaluation Conference (LREC). Portoro, Slovenia. https://doi.org/10.18653/v1/N16-1128en
dc.referencesKiritchenko, Svetlana and Mohammad, Saif M. 2016b. Sentiment composition of words with opposing polarities. In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), pp. 1102–1108. San Diego, California. https://doi.org/10.18653/v1/N16-1128en
dc.referencesKiritchenko, Svetlana, Zhu, Xiaodan and Mohammad, Saif M. 2014. Sentiment analysis of short informal texts. Journal of Artificial Intelligence Research, 50, 723–762. https://doi.org/10.1613/jair.4272en
dc.referencesLadd, D. Robert, Silverman, Kim E.A., Tolkmitt, Frank, Bergmann, Günther and Scherer, Klaus R. 1985. Evidence for the independent function of intonation contour type, voice quality, and F0 range in signaling speaker affect. The Journal of the Acoustical Society of America, 78(2), 435–444. https://doi.org/10.1121/1.392466en
dc.referencesLee, Akinobu and Kawahara, Tatsuya. 2009. Recent development of open-source speech recognition engine julius. In Proceedings: APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, 131–137. Asia-Pacific Signal and Information Processing Association, 2009 Annual ….en
dc.referencesLeinonen, Lea, Hiltunen, Tapio, Linnankoski, Ilkka and Laakso, Maija-Liisa. 1997. Expression of emotional–motivational connotations with a one-word utterance. The Journal of the Acoustical Society of America, 102(3), 1853–1863. https://doi.org/10.1121/1.420109en
dc.referencesLi, Bryan, Dimitriadis, Dimitrios and Stolcke, Andreas. 2019. Acoustic and Lexical Sentiment Analysis for Customer Service Calls. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5876–5880. IEEE. https://doi.org/10.1109/ICASSP.2019.8683679en
dc.referencesLiu, Bing. 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167. https://doi.org/10.1007/978-3-031-02145-9en
dc.referencesLiu, Bing and Zhang, Lei. 2012. A survey of opinion mining and sentiment analysis. In Mining text data, 415–463. Springer. https://doi.org/10.1007/978-1-4614-3223-4_13en
dc.referencesMairesse, François, Polifroni, Joseph and Di Fabbrizio, Giuseppe. 2012. Can prosody inform sentiment analysis? experiments on short spoken reviews. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 5093–5096. IEEE. https://doi.org/10.1109/ICASSP.2012.6289066en
dc.referencesMcAuliffe, Michael, Socolof, Michaela, Mihuc, Sarah, Wagner, Michael and Sonderegger, Morgan. 2017. Montreal Forced Aligner: Trainable Text-Speech Alignment Using Kaldi. In Interspeech, 498–502. https://doi.org/10.21437/Interspeech.2017-1386en
dc.referencesMohammad, Saif M., Kiritchenko, Svetlana and Zhu, Xiaodan. 2013. NRC-Canada: Building the state-of-the-art in sentiment analysis of tweets. ArXiv Preprint ArXiv:1308.6242.en
dc.referencesMohammad, Saif M. and Turney, Peter D. 2010. Emotions evoked by common words and phrases: Using Mechanical Turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, 26–34. LA, California: Association for Computational Linguistics.en
dc.referencesMohammad, Saif M. and Turney, Peter D. 2013. Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465. https://doi.org/10.1111/j.1467-8640.2012.00460.xen
dc.referencesPang, Bo and Lee, Lillian. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135. https://doi.org/10.1561/1500000011en
dc.referencesPeng, Zeshan. 2017. Acoustic feature-based sentiment analysis of call center data (PhD Thesis). University of Missouri–Columbia.en
dc.referencesPereira, Moisés Henrique Ramos, Pádua, Flávio Luis Cardeal, Pereira, Adriano César Machado, Benevenuto, Fabrício and Dalip, Daniel Hasan. 2016. Fusing audio, textual, and visual features for sentiment analysis of news videos. In Tenth International AAAI Conference on Web and Social Media.en
dc.referencesPérez-Rosas, Verónica, Mihalcea, Rada and Morency, Louis-Philippe. 2013. Utterance-level multimodal sentiment analysis. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 973–982.en
dc.referencesPlutchik, Robert. 1980. Emotion: A psychoevolutionary synthesis. Harper and Row.en
dc.referencesPlutchik, Robert. 1997. The circumplex as a general model of the structure of emotions and personality. In R. Plutchik and H. R. Conte (eds.), Circumplex models of personality and emotions, 7–45. Washington, DC, US: American Psychological Association. https://doi.org/10.1037/10261-001en
dc.referencesPlutchik, Robert. 2000. Emotions in the practice of psychotherapy: Clinical implications of affect theories, Vol. 13. Washington, DC, US: American Psychological Association. https://doi.org/10.1037/10366-000en
dc.referencesPlutchik, Robert. 2001a. Integration, differentiation, and derivatives of emotion. Evolution and Cognition, 7(2), 114–125.en
dc.referencesPlutchik, Robert. 2001b. The nature of emotions. American Scientist, 89(4), 344–350.en
dc.referencesR Development Core Team. 2018. R: A language and environment for statistical computing (version 3.4.4) [computer software]. Vienna, Austria. https://doi.org/10.1511/2001.4.344en
dc.referencesRazak, Aishah Abd, Abidin, Mohd Izani Zainal and Komiya, Ryoichi. 2003. Emotion pitch variation analysis in Malay and English voice samples. In The 9th Asia-Pacific Conference on Communications 2003, Vol. 1, 108–112.en
dc.referencesRibeiro, Filipe N., Araújo, Matheus, Gonçalves, Pollyanna, Gonçalves, Marcos André and Benevenuto, Fabrício. 2016. Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science, 5(1), 1–29. https://doi.org/10.1140/epjds/s13688-016-0085-1en
dc.referencesRosas, Verónica Pérez, Mihalcea, Rada and Morency, Louis-Philippe. 2013. Multimodal sentiment analysis of spanish online videos. IEEE Intelligent Systems, 28(3), 38–45. https://doi.org/10.1109/MIS.2013.9en
dc.referencesScherer, Klaus R. 1986. Vocal affect expression: A review and a model for future research. Psychological Bulletin, 99(2), 143–165. https://doi.org/10.1037/0033-2909.99.2.143en
dc.referencesSchuller, Björn, Batliner, Anton, Seppi, Dino, Steidl, Stefan, Vogt, Thurid, Wagner, Johannes, … Kessous, Loic. 2007. The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals. In Eighth Annual Conference of the International Speech Communication Association. https://doi.org/10.21437/Interspeech.2007-612en
dc.referencesSchuller, Björn, Steidl, Stefan and Batliner, Anton. 2009. The interspeech 2009 emotion challenge. In Tenth Annual Conference of the International Speech Communication Association. https://doi.org/10.21437/Interspeech.2009-103en
dc.referencesSheikh, Imran, Dumpala, Sri Harsha, Chakraborty, Rupayan and Kopparapu, Sunil Kumar. 2018. Sentiment analysis using imperfect views from spoken language and acoustic modalities. In Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML), 35–39. https://doi.org/10.18653/v1/W18-3305en
dc.referencesSkinner, E. Ray. 1935. A calibrated recording and analysis of the pitch, force and quality of vocal tones expressing happiness and sadness. Communications Monographs, 2(1), 81–137. https://doi.org/10.1080/03637753509374833en
dc.referencesSobin, Christina and Alpert, Murray. 1999. Emotion in speech: The acoustic attributes of fear, anger, sadness, and joy. Journal of Psycholinguistic Research, 28(4): 347–365. https://doi.org/10.1023/A:1023237014909en
dc.referencesSperber, Dan and Wilson, Deirdre. 1986. Relevance: Communication and Cognition. Oxford: Basil Blackwell.en
dc.referencesStolarski, Łukasz. 2018. Lack of effects of gender on the reading rate of long texts. Sociolinguistic Studies, 12(3–4), 461–479. https://doi.org/10.1558/sols.32924en
dc.referencesStolarski, Łukasz. 2020. The influence of character’s gender and the basic emotions of ‘happiness’ and ‘sadness’ on voice pitch in the reading of fiction. Brno Studies in English, 46(1), 49–89. https://doi.org/10.5817/BSE2020-1-3en
dc.referencesStolarski, Łukasz. 2021. Comparison of key statistical instruments used in lexicon-based tools for sentiment analysis in the English language. Token: A Journal of English Linguistics, 13: 219–248.en
dc.referencesTaboada, Maite, Brooke, Julian, Tofiloski, Milan, Voll, Kimberly and Stede, Manfred. 2011. Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2): 267–307. https://doi.org/10.1162/COLI_a_00049en
dc.referencesThelwall, Mike. 2017. Heart and soul: Sentiment strength detection in the social web with SentiStrength (summary book chapter). In J. Holyst (ed.), Cyberemotions: Collective emotions in cyberspace, 119–134. Berlin, Germany: Springer. https://doi.org/10.1007/978-3-319-43639-5_7en
dc.referencesThelwall, Mike and Buckley, Kevan. 2013. Topic-based sentiment analysis for the social web: The role of mood and issue-related words. Journal of the Association for Information Science and Technology, 64(8), 1608–1617. https://doi.org/10.1002/asi.22872en
dc.referencesThelwall, Mike, Buckley, Kevan and Paltoglou, Georgios. 2012. Sentiment strength detection for the social web. Journal of the Association for Information Science and Technology, 63(1), 163–173. https://doi.org/10.1002/asi.21662en
dc.referencesThelwall, Mike, Buckley, Kevan, Paltoglou, Georgios, Cai, Di, and Kappas, Arvid. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12): 2544–2558. https://doi.org/10.1002/asi.21416en
dc.referencesThelwall, Mike, Buckley, Kevan, Paltoglou, George, Skowron, Marcin, Garcia, David, Gobron, Stephane, … Holyst, Janusz A. 2013. Damping sentiment analysis in online communication: discussions, monologs and dialogs. In International Conference on Intelligent Text Processing and Computational Linguistics (pp. 1–12). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-37256-8_1en
dc.referencesTraunmüller, Hartmut and Eriksson, Anders. 1995. The frequency range of the voice fundamental in the speech of male and female adults. Unpublished Manuscript.en
dc.referencesWallbott, Harald G. and Scherer, Klaus R. 1986. Cues and channels in emotion recognition. Journal of Personality and Social Psychology, 51(4), 690–699. https://doi.org/10.1037/0022-3514.51.4.690en
dc.referencesWöllmer, Martin, Weninger, Felix, Knaup, Tobias, Schuller, Björn, Sun, Congkai, Sagae, Kenji and Morency, Louis-Philippe. 2013. Youtube movie reviews: Sentiment analysis in an audio-visual context. IEEE Intelligent Systems, 28(3), 46–53. https://doi.org/10.1109/MIS.2013.34en
dc.referencesWu, Wei, Zheng, Thomas Fang, Xu, Ming-Xing, and Bao, Huanjun. 2006. Study on speaker verification on emotional speech. In Proceedings of Ninth International Conference on Spoken Language Processing, INTERSPEECH, 2102–2105. Pittsburgh, Pennsylvania. https://doi.org/10.21437/Interspeech.2006-191en
dc.referencesZadeh, Amir, Chen, Minghai, Poria, Soujanya, Cambria, Erik, and Morency, Louis-Philippe. 2017. Tensor fusion network for multimodal sentiment analysis. ArXiv Preprint ArXiv:1707.07250. https://doi.org/10.18653/v1/D17-1115en
dc.referencesZhu, Xiaodan, Kiritchenko, Svetlana and Mohammad, Saif M. 2014. NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets. In SemEval@ COLING, 443–447. https://doi.org/10.3115/v1/S14-2077en
dc.referencesZuberbier, Erika. 1957. Zur Schreib-und Sprechmotorik der Depressiven. Zeitschrift Für Psychotherapie Und Medizinische Psychologie, 7, 239–249.en
dc.contributor.authorEmaillukasz.stolarski@ujk.edu.pl
dc.identifier.doi10.18778/1731-7533.20.2.03
dc.relation.volume20


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