Classification of Emotive Expression Using Verbal and Non Verbal Components of Speech

Dair, Zachary; Donovan, Ryan; O’Reilly, Ruairi

doi:10.1109/issc52156.2021.9467869

Cited by 3 publications

(4 citation statements)

References 24 publications

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“…In line with similar conclusions from psychology literature surrounding the importance of paralinguistic cues in the communication process, there are a number of studies employing sentiment analysis techniques that suggest a combination of text and audio data may improve classification accuracy, and consequently create a more robust representation of sentiment (Bhaskar et al, 2014; Dair et al, 2021; Houjeij et al, 2012; Yang et al, 2020). Hence, given that prior literature suggests both textual and vocal characteristics of earnings calls to be informative, and that Natural Language Processing literature finds a combination of text and audio to significantly increase classification accuracy, the adhesion of both measures represents a natural future direction for the literature.…”

Section: Discussionmentioning

confidence: 55%

“…The gold standard for multimodal sentiment analysis is considered to be the combination of all three communication modalities-text, audio and visual. Various studies have used the combination of all three modalities to define sentiment, showing that the use of a tri-modality model is more robust at classifying sentiment over bi-modal and singular modality models (Bhaskar et al, 2014;Dair et al, 2021;Houjeij et al, 2012;Morency et al, 2011;Poria et al, 2015;Yang et al, 2020). 44 The main advantage of using multimodal classifiers for sentiment classification is the additional behavioural cues provided by the visual and audio data.…”

Section: Multimodal Analysismentioning

confidence: 99%

“…They note that there is a scarcity of advanced NLP techniques being applied in the financial domain 32 . While the ML techniques discussed in the previous section have been shown to classify financial sentiment better than more rudimentary approaches, alternative approaches such as transformer architecture (Alamoudi & Alghamdi, 2021; Munikar et al, 2019; Sun et al, 2019) and multimodal analysis (Bhaskar et al, 2014; Dair et al, 2021; Houjeij et al, 2012; Yang et al, 2020) have been demonstrated as having greater abilities in accurately capturing sentiment.…”

Section: Applications Of Sentiment Analysis In Financementioning

confidence: 99%

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Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Todd,

Bowden,

Moshfeghi

2024

Intell Sys Acc Fin Mgmt

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SummaryAdvances in Deep Learning have drastically improved the abilities of Natural Language Processing (NLP) research, creating new state‐of‐the‐art benchmarks. Two research streams at the forefront of NLP analysis are transformer architecture and multimodal analysis. This paper critically evaluates the extant literature applying sentiment analysis techniques to the financial domain. We classify the financial sentiment analysis literature according to the most used techniques in the area, with a focus on methods used to detect sentiment within corporate earnings conference calls, because of their dual modality (text‐audio) nature. We find that the financial literature follows a similar path to NLP sentiment literature, in that more advanced techniques to define sentiment are being used as the field progresses. However, techniques used to determine financial sentiment currently fall behind state‐of‐the‐art techniques used within NLP. Two future directions stem from this paper. Firstly, we propose that the adoption of transformer architecture to create robust representations of textual data could enhance sentiment analysis in academic finance. Secondly, the adoption of multimodal classifiers in finance represents a new, currently underexplored area of study that offers opportunities for finance research.

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Section: Discussionmentioning

confidence: 55%

Section: Multimodal Analysismentioning

confidence: 99%

Section: Applications Of Sentiment Analysis In Financementioning

confidence: 99%

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Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Todd,

Bowden,

Moshfeghi

2024

Intell Sys Acc Fin Mgmt

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“…The architecture of the CNN used for classification is depicted in Figure 1. A CNN was re-implemented, due to the high emotive expression classification performance using spectral features as exhibited in [6]. Optimal hyper-parameters were identified from a comparative analysis against related approaches.…”

Section: Classification Of Emotive Speechmentioning

confidence: 99%

Linguistic and Gender Variation in Speech Emotion Recognition using Spectral Features

Dair¹,

Donovan²,

O’Reilly³

2021

Preprint

Self Cite

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This work explores the effect of gender and linguistic-based vocal variations on the accuracy of emotive expression classification. Emotive expressions are considered from the perspective of spectral features in speech (Mel-frequency Cepstral Coefficient, Melspectrogram, Spectral Contrast). Emotions are considered from the perspective of Basic Emotion Theory. A convolutional neural network is utilised to classify emotive expressions in emotive audio datasets in English, German, and Italian. Vocal variations for spectral features assessed by (i) a comparative analysis identifying suitable spectral features, (ii) the classification performance for mono, multi and cross-lingual emotive data and (iii) an empirical evaluation of a machine learning model to assess the effects of gender and linguistic variation on classification accuracy. The results showed that spectral features provide a potential avenue for increasing emotive expression classification. Additionally, the accuracy of emotive expression classification was high within mono and cross-lingual emotive data, but poor in multi-lingual data. Similarly, there were differences in classification accuracy between gender populations. These results demonstrate the importance of accounting for population differences to enable accurate speech emotion recognition.

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A Semantic Approach for Computing Speech Emotion Text Classification Using Machine Learning Algorithms

Jacob

2022

2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)

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Classification of Emotive Expression Using Verbal and Non Verbal Components of Speech

Cited by 3 publications

References 24 publications

Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Text‐based sentiment analysis in finance: Synthesising the existing literature and exploring future directions

Linguistic and Gender Variation in Speech Emotion Recognition using Spectral Features

A Semantic Approach for Computing Speech Emotion Text Classification Using Machine Learning Algorithms

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