Exploring the contextual factors affecting multimodal emotion recognition in videos Article Swipe
Prasanta Bhattacharya
,
Raj Kumar Gupta
,
Yinping Yang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.13274
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2004.13274
Emotional expressions form a key part of user behavior on today's digital platforms. While multimodal emotion recognition techniques are gaining research attention, there is a lack of deeper understanding on how visual and non-visual features can be used to better recognize emotions in certain contexts, but not others. This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) gender of the speaker, and ii) duration of the emotional episode. Using a large public dataset of 2,176 manually annotated YouTube videos, we found that while multimodal features consistently outperformed bimodal and unimodal features, their performance varied significantly across different emotions, gender and duration contexts. Multimodal features performed particularly better for male speakers in recognizing most emotions. Furthermore, multimodal features performed particularly better for shorter than for longer videos in recognizing neutral and happiness, but not sadness and anger. These findings offer new insights towards the development of more context-aware emotion recognition and empathetic systems.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2004.13274
- https://arxiv.org/pdf/2004.13274
- OA Status
- green
- Cited By
- 5
- References
- 82
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3139316640
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3139316640Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2004.13274Digital Object Identifier
- Title
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Exploring the contextual factors affecting multimodal emotion recognition in videosWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-04-28Full publication date if available
- Authors
-
Prasanta Bhattacharya, Raj Kumar Gupta, Yinping YangList of authors in order
- Landing page
-
https://arxiv.org/abs/2004.13274Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2004.13274Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2004.13274Direct OA link when available
- Concepts
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Sadness, Anger, Happiness, Emotion recognition, Context (archaeology), Facial expression, Psychology, Emotion classification, Cognitive psychology, Key (lock), Computer science, Speech recognition, Social psychology, Communication, Computer security, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2023: 1, 2022: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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82Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2020-04-28 |
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| referenced_works | https://openalex.org/W2510285366, https://openalex.org/W1871419576, https://openalex.org/W2114785251, https://openalex.org/W2070710240, https://openalex.org/W1668904664, https://openalex.org/W2079404915, https://openalex.org/W2154611638, https://openalex.org/W2767415038, https://openalex.org/W2745497104, https://openalex.org/W2963177779, https://openalex.org/W2156848952, https://openalex.org/W1966797434, https://openalex.org/W2168465881, https://openalex.org/W2117645142, https://openalex.org/W3096195729, https://openalex.org/W1999543972, https://openalex.org/W2106508100, https://openalex.org/W2083543775, https://openalex.org/W2291864985, https://openalex.org/W1985867508, https://openalex.org/W2042684846, https://openalex.org/W1984544628, https://openalex.org/W2150150736, https://openalex.org/W2253728219, https://openalex.org/W104703790, https://openalex.org/W1994720412, https://openalex.org/W1915059114, https://openalex.org/W2991863245, https://openalex.org/W2009375902, https://openalex.org/W1498818117, https://openalex.org/W207186943, https://openalex.org/W2149628368, https://openalex.org/W2475772268, https://openalex.org/W2158180452, https://openalex.org/W2963199341, https://openalex.org/W2080563528, https://openalex.org/W2001483327, https://openalex.org/W149760548, https://openalex.org/W2137363687, https://openalex.org/W2058616948, https://openalex.org/W2990474076, https://openalex.org/W2557887185, https://openalex.org/W2767270377, https://openalex.org/W2164699598, https://openalex.org/W1651753422, https://openalex.org/W2170085344, https://openalex.org/W2897760566, https://openalex.org/W2130162821, https://openalex.org/W1977137834, https://openalex.org/W5604351, https://openalex.org/W2547683630, https://openalex.org/W1963947028, https://openalex.org/W2941153755, https://openalex.org/W2521562261, https://openalex.org/W2058006394, https://openalex.org/W2586527019, https://openalex.org/W2747764356, https://openalex.org/W2890732027, https://openalex.org/W1995641269, https://openalex.org/W1497399621, https://openalex.org/W1986486674, https://openalex.org/W2148143831, https://openalex.org/W2063262826, https://openalex.org/W2171939880, https://openalex.org/W2805939548, https://openalex.org/W3137861108, https://openalex.org/W2897322946, https://openalex.org/W2030931454, https://openalex.org/W2607119677, https://openalex.org/W2981530776, https://openalex.org/W2026243162, https://openalex.org/W2143350951, https://openalex.org/W2897690397, https://openalex.org/W2745880412, https://openalex.org/W2164471543, https://openalex.org/W1965281242, https://openalex.org/W1967684049, https://openalex.org/W1508580449, https://openalex.org/W1660030754, https://openalex.org/W2055332436, https://openalex.org/W2609563119, https://openalex.org/W2548128734 |
| referenced_works_count | 82 |
| abstract_inverted_index.a | 3, 24, 87 |
| abstract_inverted_index.be | 36 |
| abstract_inverted_index.i) | 74 |
| abstract_inverted_index.in | 42, 67, 128, 144 |
| abstract_inverted_index.is | 23 |
| abstract_inverted_index.of | 6, 26, 56, 76, 82, 91, 162 |
| abstract_inverted_index.on | 9, 29 |
| abstract_inverted_index.to | 38 |
| abstract_inverted_index.we | 97 |
| abstract_inverted_index.and | 32, 65, 79, 106, 117, 147, 152, 167 |
| abstract_inverted_index.are | 18 |
| abstract_inverted_index.but | 45, 149 |
| abstract_inverted_index.can | 35 |
| abstract_inverted_index.for | 125, 138, 141 |
| abstract_inverted_index.how | 30 |
| abstract_inverted_index.ii) | 80 |
| abstract_inverted_index.key | 4, 71 |
| abstract_inverted_index.new | 157 |
| abstract_inverted_index.not | 46, 150 |
| abstract_inverted_index.the | 51, 54, 77, 83, 160 |
| abstract_inverted_index.two | 70 |
| abstract_inverted_index.This | 48 |
| abstract_inverted_index.form | 2 |
| abstract_inverted_index.from | 61 |
| abstract_inverted_index.lack | 25 |
| abstract_inverted_index.male | 126 |
| abstract_inverted_index.more | 163 |
| abstract_inverted_index.most | 130 |
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| abstract_inverted_index.text | 66 |
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| abstract_inverted_index.that | 99 |
| abstract_inverted_index.tone | 64 |
| abstract_inverted_index.used | 37 |
| abstract_inverted_index.user | 7 |
| abstract_inverted_index.with | 69 |
| abstract_inverted_index.2,176 | 92 |
| abstract_inverted_index.These | 154 |
| abstract_inverted_index.Using | 86 |
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| abstract_inverted_index.found | 98 |
| abstract_inverted_index.large | 88 |
| abstract_inverted_index.offer | 156 |
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| abstract_inverted_index.their | 109 |
| abstract_inverted_index.there | 22 |
| abstract_inverted_index.while | 100 |
| abstract_inverted_index.across | 113 |
| abstract_inverted_index.anger. | 153 |
| abstract_inverted_index.better | 39, 124, 137 |
| abstract_inverted_index.deeper | 27 |
| abstract_inverted_index.facial | 62 |
| abstract_inverted_index.gender | 75, 116 |
| abstract_inverted_index.longer | 142 |
| abstract_inverted_index.public | 89 |
| abstract_inverted_index.varied | 111 |
| abstract_inverted_index.videos | 143 |
| abstract_inverted_index.visual | 31 |
| abstract_inverted_index.YouTube | 95 |
| abstract_inverted_index.between | 53 |
| abstract_inverted_index.bimodal | 105 |
| abstract_inverted_index.certain | 43 |
| abstract_inverted_index.dataset | 90 |
| abstract_inverted_index.derived | 60 |
| abstract_inverted_index.digital | 11 |
| abstract_inverted_index.effects | 55 |
| abstract_inverted_index.emotion | 15, 58, 165 |
| abstract_inverted_index.gaining | 19 |
| abstract_inverted_index.neutral | 146 |
| abstract_inverted_index.others. | 47 |
| abstract_inverted_index.sadness | 151 |
| abstract_inverted_index.shorter | 139 |
| abstract_inverted_index.today's | 10 |
| abstract_inverted_index.towards | 159 |
| abstract_inverted_index.videos, | 96 |
| abstract_inverted_index.analyzes | 50 |
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| abstract_inverted_index.duration | 81, 118 |
| abstract_inverted_index.emotions | 41 |
| abstract_inverted_index.episode. | 85 |
| abstract_inverted_index.factors: | 73 |
| abstract_inverted_index.features | 34, 59, 102, 121, 134 |
| abstract_inverted_index.findings | 155 |
| abstract_inverted_index.insights | 158 |
| abstract_inverted_index.manually | 93 |
| abstract_inverted_index.research | 20 |
| abstract_inverted_index.speaker, | 78 |
| abstract_inverted_index.speakers | 127 |
| abstract_inverted_index.systems. | 169 |
| abstract_inverted_index.unimodal | 107 |
| abstract_inverted_index.Emotional | 0 |
| abstract_inverted_index.annotated | 94 |
| abstract_inverted_index.contexts, | 44 |
| abstract_inverted_index.contexts. | 119 |
| abstract_inverted_index.different | 114 |
| abstract_inverted_index.emotional | 84 |
| abstract_inverted_index.emotions, | 115 |
| abstract_inverted_index.emotions. | 131 |
| abstract_inverted_index.features, | 108 |
| abstract_inverted_index.interplay | 52 |
| abstract_inverted_index.performed | 122, 135 |
| abstract_inverted_index.recognize | 40 |
| abstract_inverted_index.Multimodal | 120 |
| abstract_inverted_index.attention, | 21 |
| abstract_inverted_index.contextual | 72 |
| abstract_inverted_index.empathetic | 168 |
| abstract_inverted_index.happiness, | 148 |
| abstract_inverted_index.multimodal | 14, 57, 101, 133 |
| abstract_inverted_index.non-visual | 33 |
| abstract_inverted_index.platforms. | 12 |
| abstract_inverted_index.techniques | 17 |
| abstract_inverted_index.conjunction | 68 |
| abstract_inverted_index.development | 161 |
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| abstract_inverted_index.expressions, | 63 |
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| abstract_inverted_index.particularly | 123, 136 |
| abstract_inverted_index.context-aware | 164 |
| abstract_inverted_index.significantly | 112 |
| abstract_inverted_index.understanding | 28 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/5 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Gender equality |
| citation_normalized_percentile |