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Infer Induced Sentiment of Comment Response to Video: A New Task, Dataset and Baseline
Computer Vision and Pattern Recognition · Submitted 15 May 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Extensive experiments demonstrate that our method is showing significant improvements over other established baselines.
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
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The abstract, sentence by sentence
Existing video multi-modal sentiment analysis mainly focuses on the sentiment expression of people within the video, yet often neglects the induced sentiment of viewers while watching the videos. Induced sentiment of viewers is essential for inferring the public response to videos, has broad application in analyzing public societal sentiment, effectiveness of advertising and other areas. The micro videos and the related comments provide a rich application scenario for viewers induced sentiment analysis. In light of this, we introduces a novel research task, Multi-modal Sentiment Analysis for Comment Response of Video Induced(MSA-CRVI), aims to inferring opinions and emotions according to the comments response to micro video. Meanwhile, we manually annotate a dataset named Comment Sentiment toward to Micro Video (CSMV) to support this research. It is the largest video multi-modal sentiment dataset in terms of scale and video duration to our knowledge, containing 107,267 comments and 8,210 micro videos with a video duration of 68.83 hours. To infer the induced sentiment of comment should leverage the video content, so we propose the Video Content-aware Comment Sentiment Analysis (VC-CSA) method as baseline to address the challenges inherent in this new task. Extensive experiments demonstrate that our method is showing significant improvements over other established baselines.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Existing video multi-modal sentiment analysis mainly focuses on the sentiment expression of people within the video, yet often neglects the induced sentiment of viewers while watching the videos. | gap 0.73 | 0.03 | 0.03 | asserted 0.99 |
| s1 | Induced sentiment of viewers is essential for inferring the public response to videos, has broad application in analyzing public societal sentiment, effectiveness of advertising and other areas. | background 0.97 | 0.05 | 0.06 | asserted 1.00 |
| s2 | The micro videos and the related comments provide a rich application scenario for viewers induced sentiment analysis. | background 0.98 | 0.07 | 0.02 | asserted 1.00 |
| s3 | In light of this, we introduces a novel research task, Multi-modal Sentiment Analysis for Comment Response of Video Induced(MSA-CRVI), aims to inferring opinions and emotions according to the comments response to micro video. | contribution 1.00 | 0.84 | 0.09 | asserted 0.96 |
| s4 | Meanwhile, we manually annotate a dataset named Comment Sentiment toward to Micro Video (CSMV) to support this research. | contribution 0.89 | 0.96 | 0.03 | asserted 1.00 |
| s5 | It is the largest video multi-modal sentiment dataset in terms of scale and video duration to our knowledge, containing 107,267 comments and 8,210 micro videos with a video duration of 68.83 hours. | contribution 0.88 | 0.92 | 0.04 | asserted 0.98 |
| s6 | To infer the induced sentiment of comment should leverage the video content, so we propose the Video Content-aware Comment Sentiment Analysis (VC-CSA) method as baseline to address the challenges inherent in this new task. | method 0.82 | 0.86 | 0.07 | asserted 0.95 |
| s7 | Extensive experiments demonstrate that our method is showing significant improvements over other established baselines. | result 1.00 | 0.95 | 0.76 | asserted 1.00 |
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The paper
Original abstract
Existing video multi-modal sentiment analysis mainly focuses on the sentiment expression of people within the video, yet often neglects the induced sentiment of viewers while watching the videos. Induced sentiment of viewers is essential for inferring the public response to videos, has broad application in analyzing public societal sentiment, effectiveness of advertising and other areas. The micro videos and the related comments provide a rich application scenario for viewers induced sentiment analysis. In light of this, we introduces a novel research task, Multi-modal Sentiment Analysis for Comment Response of Video Induced(MSA-CRVI), aims to inferring opinions and emotions according to the comments response to micro video. Meanwhile, we manually annotate a dataset named Comment Sentiment toward to Micro Video (CSMV) to support this research. It is the largest video multi-modal sentiment dataset in terms of scale and video duration to our knowledge, containing 107,267 comments and 8,210 micro videos with a video duration of 68.83 hours. To infer the induced sentiment of comment should leverage the video content, so we propose the Video Content-aware Comment Sentiment Analysis (VC-CSA) method as baseline to address the challenges inherent in this new task. Extensive experiments demonstrate that our method is showing significant improvements over other established baselines.
Human review
How the literature uses it
What this paper does with the arXiv papers it cites
What it cites
Uses the method or tool of 1 paper
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The comment text is encoded by a RoBERTa [ref] language pre-trained model to extract text features from the comment.
RoBERTa: A Robustly Optimized BERT Pretraining Approach · Method · 1.00
Compares with 3 papers
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Notably, our selection included methods that primarily utilize textual input, such as BERT [ref] and RoBERTa [ref] .
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding · Experiments · 0.98
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Notably, our selection included methods that primarily utilize textual input, such as BERT [ref] and RoBERTa [ref] .
RoBERTa: A Robustly Optimized BERT Pretraining Approach · Experiments · 0.98
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Furthermore, we select several typical traditional multi-modal sentiment analysis methods: TBJE [ref] , SELF-MM [ref] , MISA [ref] , MMIM [ref] and CubeMLP [ref] .
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis · Experiments · 0.98
Cites as background 2 papers
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Delbrouck et al. [ref] proposed a transformer-based joint-encoding method employing cross-modal attention mechanisms to capture inter-modality interactions.
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis · Related work · 0.83
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Md Shad Akhtar et al. [ref]
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis · Related work · 0.97
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