Content-Based Prediction of Movie Style, Aesthetics, and Affect: Data Set and Baseline Experiments

Tarvainen, Jussi; Sjöberg, Mats; Westman, Stina; Laaksonen, Jorma; Oittinen, Pirkko

doi:10.1109/tmm.2014.2357688

Cited by 34 publications

(21 citation statements)

References 56 publications

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“…Although consistencies between perceived and induced emotions have been found [16], music emotion research has identified fundamental differences between perceived and induced emotions (e.g. [5] and [17]). Previous work has also suggested that induced emotions can have more intensive arousal and less intensive valence ratings compared to perceived emotions of the same stimuli [4].…”

Section: Perceived Vs Induced Emotionsmentioning

confidence: 99%

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Tian

Muszyński

Lai

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-Predicting the emotional response of movie audiences to affective movie content is a challenging task in affective computing. Previous work has focused on using audiovisual movie content to predict movie induced emotions. However, the relationship between the audience's perceptions of the affective movie content (perceived emotions) and the emotions evoked in the audience (induced emotions) remains unexplored. In this work, we address the relationship between perceived and induced emotions in movies, and identify features and modelling approaches effective for predicting movie induced emotions. First, we extend the LIRIS-ACCEDE database by annotating perceived emotions in a crowd-sourced manner, and find that perceived and induced emotions are not always consistent. Second, we show that dialogue events and aesthetic highlights are effective predictors of movie induced emotions. In addition to movie based features, we also study physiological and behavioural measurements of audiences. Our experiments show that induced emotion recognition can benefit from including temporal context and from including multimodal information. Our study bridges the gap between affective content analysis and induced emotion prediction.

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Section: Perceived Vs Induced Emotionsmentioning

confidence: 99%

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Tian

Muszyński

Lai

et al. 2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)

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“…In [79], textual analysis results are utilized as weak labels for pre-training CNN, which is further turned using annotated 1,269 Twitter images. Other efforts on dataset construction for affective computing include video emotion detection [22] and aesthetics analysis [63,38]. These datasets are either too small or not able to be directly used for sentiment analysis.…”

Section: Predicting Sentiment In Social Datamentioning

confidence: 99%

“…Meanwhile, different from the concrete concept detection, affective analysis targets at more abstract human concepts. The additional challenges motivate the design of new visual features based on aesthetics, psychological and art theory [34,24,82,63,69].…”

Section: Visual Sentiment Analysismentioning

confidence: 99%

Sentiment Analysis on Multi-View Social Data

Niu

Zhu

Pang

et al. 2016

Lecture Notes in Computer Science

126

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With the proliferation of social networks, people are likely to share their opinions about news, social events and products on the Web. There is an increasing interest in understanding users' attitude or sentiment from the large repository of opinion-rich data on the Web. This can benefit many commercial and political applications. Primarily, the researchers concentrated on the documents such as users' comments on the purchased products. Recent works show that visual appearance also conveys rich human affection that can be predicted. While great efforts have been devoted on the single media, either text or image, little attempts are paid for the joint analysis of multi-view data which is becoming a prevalent form in the social media. For example, paired with the posted textual messages on Twitter, users are likely to upload images and videos which may carry their affective states. One common obstacle is the lack of sufficient manually annotated instances for model learning and performance evaluation. To prompt the researches on this problem, we introduce a multi-view sentiment analysis dataset (MVSA) including a set of manually annotated image-text pairs collected from Twitter. The dataset can be utilized as a valuable benchmark for both single-view and multi-view sentiment analysis. In this thesis, we further conduct a comprehensive study on computational analysis of sentiment from the multi-view data. The state-of-the-art approaches on single view (image or text) or multiview (image and text) data are introduced, and compared through extensive experiments conducted on our constructed dataset and other public datasets. More importantly, the effectiveness of the correlation between different views is also studied using the widely used fusion strategies and advanced multi-view feature extraction methods.Index Terms: Sentiment analysis, social media, multi-view data, textual feature, visual feature, joint feature learning.

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“…This is equally the case with affective video datasets (e.g., [19], [20], [21], [22], [23]). However prior research shows that individual differences can lead to varied experiences [5].…”

Section: Related Workmentioning

confidence: 99%

Modelling the influence of personality and culture on affect and enjoyment in multimedia

Guntuku

Wang

Scott

et al. 2015

2015 International Conference on Affective Computing and Intelligent Interaction (ACII)

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Abstract-Affect is evoked through an intricate relationship between the characteristics of stimuli, individuals, and systems of perception. While affect is widely researched, few studies consider the combination of multimedia system characteristics and human factors together. As such, this paper explores the influence of personality (Five-Factor Model) and cultural traits (Hofstede Model) on the intensity of multimedia-evoked positive and negative affects (emotions). A set of 144 video sequences (from 12 short movie clips) were evaluated by 114 participants from a cross-cultural population, producing 1232 ratings. On this data, three multilevel regression models are compared: a baseline model that only considers system factors; an extended model that includes personality and culture; and an optimistic model in which each participant is modelled. An analysis shows that personal and cultural traits represent 5.6% of the variance in positive affect and 13.6% of the variance in negative affect. In addition, the affect-enjoyment correlation varied across the clips. This suggests that personality and culture play a key role in predicting the intensity of negative affect and whether or not it is enjoyed, but a more sophisticated set of predictors is needed to model positive affect with the same efficacy.

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Content-Based Prediction of Movie Style, Aesthetics, and Affect: Data Set and Baseline Experiments

Cited by 34 publications

References 56 publications

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Recognizing induced emotions of movie audiences: Are induced and perceived emotions the same?

Sentiment Analysis on Multi-View Social Data

Modelling the influence of personality and culture on affect and enjoyment in multimedia

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