Sentiment Analysis and Topic Recognition in Video Transcriptions

Stappen, Lukas; Baird, Alice; Wang, Zhaoxia; Schuller, Björn

doi:10.1109/mis.2021.3062200

Cited by 94 publications

(17 citation statements)

References 24 publications

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“…Ref. [45] presents a sentiment analysis study focusing on topics and emotions in video car reviews from YouTube; in this analysis, an SVM was used as the classification model. In [46], a review of major emotion models is conducted and a new version of the Hourglass model, which is a biologically inspired and psychologically motivated emotion categorization model for sentiment analysis, is proposed.…”

Section: Recent Sentiment Analysis Approachesmentioning

confidence: 99%

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

2022

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Constitutional processes are a cornerstone of modern democracies. Whether revolutionary or institutionally organized, they establish the core values of social order and determine the institutional architecture that governs social life. Constitutional processes are themselves evolutionary practices of mutual learning in which actors, regardless of their initial political positions, continuously interact with each other, demonstrating differences and making alliances regarding different topics. In this article, we develop Tree Augmented Naive Bayes (TAN) classifiers to model the behavior of constituent agents. According to the nature of the constituent dynamics, weights are learned by the model from the data using an evolution strategy to obtain a good classification performance. For our analysis, we used the constituent agents’ communications on Twitter during the installation period of the Constitutional Convention (July–October 2021). In order to differentiate political positions (left, center, right), we applied the developed algorithm to obtain the scores of 882 ballots cast in the first stage of the convention (4 July to 29 September 2021). Then, we used k-means to identify three clusters containing right-wing, center, and left-wing positions. Experimental results obtained using the three constructed datasets showed that using alternative weight values in the TAN construction procedure, inferred by an evolution strategy, yielded improvements in the classification accuracy measured in the test sets compared to the results of the TAN constructed with conditional mutual information, as well as other Bayesian network classifier construction approaches. Additionally, our results may help us to better understand political behavior in constitutional processes and to improve the accuracy of TAN classifiers applied to social, real-world data.

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Section: Recent Sentiment Analysis Approachesmentioning

confidence: 99%

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

2022

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“…As a core branch of sentiment analysis [11,50], multimodal sentiment analysis has attracted significant attention in recent years [21,27,32,41]. Compared to a single modality case, multimodal sentiment analysis is more challenging due to the complexity of handling and analyzing data from different modalities.…”

Section: Multimodal Sentiment Analysismentioning

confidence: 99%

Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities

Zeng¹,

Liu²,

Zhou³

2022

Preprint

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Multimodal sentiment analysis has been studied under the assumption that all modalities are available. However, such a strong assumption does not always hold in practice, and most of multimodal fusion models may fail when partial modalities are missing. Several works have addressed the missing modality problem; but most of them only considered the single modality missing case, and ignored the practically more general cases of multiple modalities missing. To this end, in this paper, we propose a Tag-Assisted Transformer Encoder (TATE) network to handle the problem of missing uncertain modalities. Specifically, we design a tag encoding module to cover both the single modality and multiple modalities missing cases, so as to guide the network's attention to those missing modalities. Besides, we adopt a new space projection pattern to align common vectors. Then, a Transformer encoder-decoder network is utilized to learn the missing modality features. At last, the outputs of the Transformer encoder are used for the final sentiment classification. Extensive experiments are conducted on CMU-MOSI and IEMO-CAP datasets, showing that our method can achieve significant improvements compared with several baselines. CCS CONCEPTS• Information System → Multimodal Sentiment Analysis.

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“…Today, research is being carried out on recognizing facial emotions from videos [4][5][6][7][8][9][10][11][12][13][14], on determining emotions by voice rh2ythm from audio information [15][16][17][18], and by writing style from texts [19][20][21]. In the following works [22,23], a deep convolutional neural network is used to recognize facial emotions from videos and images.…”

Section: Introductionmentioning

confidence: 99%

Emotional Speech Recognition Method Based on Word Transcription

Bekmanova

Yergesh

Sharipbay

et al. 2022

Sensors

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The emotional speech recognition method presented in this article was applied to recognize the emotions of students during online exams in distance learning due to COVID-19. The purpose of this method is to recognize emotions in spoken speech through the knowledge base of emotionally charged words, which are stored as a code book. The method analyzes human speech for the presence of emotions. To assess the quality of the method, an experiment was conducted for 420 audio recordings. The accuracy of the proposed method is 79.7% for the Kazakh language. The method can be used for different languages and consists of the following tasks: capturing a signal, detecting speech in it, recognizing speech words in a simplified transcription, determining word boundaries, comparing a simplified transcription with a code book, and constructing a hypothesis about the degree of speech emotionality. In case of the presence of emotions, there occurs complete recognition of words and definitions of emotions in speech. The advantage of this method is the possibility of its widespread use since it is not demanding on computational resources. The described method can be applied when there is a need to recognize positive and negative emotions in a crowd, in public transport, schools, universities, etc. The experiment carried out has shown the effectiveness of this method. The results obtained will make it possible in the future to develop devices that begin to record and recognize a speech signal, for example, in the case of detecting negative emotions in sounding speech and, if necessary, transmitting a message about potential threats or riots.

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Sentiment Analysis and Topic Recognition in Video Transcriptions

Cited by 94 publications

References 24 publications

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers

Tag-assisted Multimodal Sentiment Analysis under Uncertain Missing Modalities

Emotional Speech Recognition Method Based on Word Transcription

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