Extraction of unconscious emotions while watching TV commercials

Nomura, Tomomi; Mitsukura, Yasue

doi:10.1109/iecon.2015.7392127

Cited by 9 publications

(8 citation statements)

References 6 publications

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“…Regarding the first hypothesis, the results confirm that videos with surprising elements and those triggering joy have a high presence in the list of most successful ones. In this sense, the results are consistent with the research by Dobele et al (2007), Eckler & Bolls (2011), Teixeira, Wedel & Pieters (2012), Guadagno, Rempala, Murphy & Okdie (2013), Dafonte-Gómez (2014) or Nomura & Mitsukura (2015). The presence of negative emotions is practically residual in the sample and almost non-existent in the most widely shared videos, except for the video with more shares, where sadness is combined with joy, an effect Teixeira (2012) refers to as an "emotional roller coaster" and which she thinks is very efficient in terms of virality.…”

Section: Conclusion and Discussionsupporting

confidence: 85%

“…Viral Plummer, 2010). Emotional content also has an impact on the attention paid to advertising (Nomura & Mitsukura, 2015;Teixeira, 2012;Vecchiato et al, 2010). Teixeira, Wedel and Pieters (2012) confirm that -mainly-surprise and -to a lesser extent-joy influence the attention paid by viewers to spots and that both emotions have an impact on the decision to continue watching a commercial; in this case joy has more weight on the decision.…”

Section: Introductionmentioning

confidence: 89%

See 1 more Smart Citation

Viral Dissemination of Content in Advertising: Emotional Factors to Reach Consumers

Gómez¹,

González²,

Valencia³

2020

Communication & Society

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Section: Conclusion and Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 89%

Viral Dissemination of Content in Advertising: Emotional Factors to Reach Consumers

Gómez¹,

González²,

Valencia³

2020

Communication & Society

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“…Then, the signal was extracted in alpha waves 9-14 Hz with five steps for getting into 12 points. Beta wave extraction was after four steps (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32), gave 36 points. After wavelet filtering, each second, using wavelet extraction we reduced 128 points into 56 points data.…”

Section: B Wavelet Extractionmentioning

confidence: 99%

“…EEG signals involve a great deal of information about the function of the brain, which may reflect a state of mind, such as level of attention [2], relax condition [3], mental activity [4], human grasping [5] [6], human attention [7], alertness level [8], or emotional conditions [9], [1], [10], and [11], [12]- [18]. Several studies on identification of emotional states through EEG signals are the response of sound stimulation [19], after watching movies [20], watching ads [21], listening to music [11], playing video game [12] , and watching videos [22], [23]. Moreover, wireless EEG provide comfort so emotional identification of brain signals can be an intermediate device in the development of Brain Computer Interface (BCI) [24] [25].…”

Section: Introductionmentioning

confidence: 99%

EEG Based Emotion Monitoring Using Wavelet and Learning Vector Quantization

Djamal¹,

Lodaya²

2017

EECSI

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Emotional identification is necessary for example in Brain Computer Interface (BCI) application and when emotional therapy and medical rehabilitation take place. Some emotional states can be characterized in the frequency of EEG signal, such excited, relax and sad. The signal extracted in certain frequency useful to distinguish the three emotional state. The classification of the EEG signal in real time depends on extraction methods to increase class distinction, and identification methods with fast computing. This paper proposed human emotion monitoring in real time using Wavelet and Learning Vector Quantization (LVQ). The process was done before the machine learning using training data from the 10 subjects, 10 trial, 3 classes and 16 segments (equal to 480 sets of data). Each data set processed in 10 seconds and extracted into Alpha, Beta, and Theta waves using Wavelet. Then they become input for the identification system using LVQ three emotional state that is excited, relax, and sad. The results showed that by using wavelet we can improve the accuracy of 72% to 87% and number of training data variation increased the accuracy. The system was integrated with wireless EEG to monitor emotion state in real time with change each 10 seconds. It takes 0.44 second, was not significant toward 10 seconds.

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“…In this way, advertisements using emotional content generate a deeper connection between the consumer and the brand than those using rational argumentation (Micu & Plummer, 2010). Emotional content also has an impact on the attention paid to advertising (Nomura & Mitsukura, 2015;Teixeira, 2012;Giovanni Vecchiato et al, 2010). Teixeira, Wedel & Pieters, (2012) confirm that -mainlysurprise and -to a lesser extent-joy influence the attention paid by viewers to spots and that both emotions have an impact on the decision to continue watching a commercial; in this case joy has more weight on the decision.…”

Section: Introductionmentioning

confidence: 99%

Viral Dissemination of Content in Advertising: Emotional Factors to Reach Consumers

Gómez¹,

González²,

Valencia³

2020

Preprint

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Social media has become a relevant content dissemination channel in recent years. Each user has the capacity to potentially reach others, and in this way, social media challenges the traditional distribution of content through mass media. The advertising industry has had to adapt to this new ecosystem and develop audiovisual pieces specifically tailored to this environment as part of their communication strategies. These pieces aim at achieving not only views but to engage viewers in sharing content with their contacts. Recent research indicates that there are certain aspects of human behaviour related to emotions and motivations that have an impact on the decision to share information, news and content with others. However, there are few studies analysing how the features of the content shared influence that decision. This article presents the content analysis results of the 100 most widely shared advertising viral videos around the world from 2011 to 2015 according to Adweek. The analysis was conducted from an emotional perspective in order to establish the common features of the most shared videos and to identify the frequency in which emotional narrative resources are used. Key wordsAdvertising, viral video, social media, content analysis, emotions by the user. Viral marketing appeals to the audience's interest to share content and uses existing distribution networks that the advertiser does not need to own (Welker, 2002).Despite the fact that the motivations of individuals are not homogeneous (Amichai-Hamburger, 2002), there is some consensus around the influence of three personality dimensions that affect on-line communications and relationships: openness, understood as a creative, innovative and tolerant attitude; extraversion, understood as sociability, assertiveness and enthusiasm; and neuroticism (related to emotional stability and selfconfidence) (

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Extraction of unconscious emotions while watching TV commercials

Cited by 9 publications

References 6 publications

Viral Dissemination of Content in Advertising: Emotional Factors to Reach Consumers

Viral Dissemination of Content in Advertising: Emotional Factors to Reach Consumers

EEG Based Emotion Monitoring Using Wavelet and Learning Vector Quantization

Viral Dissemination of Content in Advertising: Emotional Factors to Reach Consumers

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