Multimodal Sentiment Analysis

Kaur, Ramandeep; Kautish, Sandeep

doi:10.4018/978-1-6684-6303-1.ch098

Cited by 46 publications

(15 citation statements)

References 97 publications

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“…In Eq (3), H m represents the output of the embedding layer. The parameters of the embedding layer are learnable, with the purpose of mapping input vectors to a high-dimensional space.…”

Section: Feature Masking Text Embedding Module (Ft)mentioning

confidence: 99%

“…Multi-modal data encompasses a wealth of information, including facial expressions, auditory cues, and language, which collectively constitute a comprehensive expression for accurately depicting the genuine emotional state. The absence of any of these information streams would lead to an incomplete representation, thereby hindering the ability to fully capture the emotional state in question 3 . For instance, the sentence "The sun is really bright today" poses a challenge in determining the associated emotional category when relying solely on textual information.…”

Section: Introductionmentioning

confidence: 99%

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A multimodal sentiment model based on causal gating attention mechanism

Gan,

Sun,

Liu

2024

Preprint

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Utilising a cross-modal attention mechanism for multimodal feature integration may engender confounding effects, resulting indetrimental biases during modal interaction, and consequently impacting the outcomes of emotion classification. To addressthis issue, a cross-modal fusion network based on causal gating attention mechanism was proposed. First, a feature-maskingtext embedding module is utilised to enhance the semantic representation capability of both the audio and video modalities.Subsequently, a cross-modal attention fusion module is employed to complementarily merge the audio and video modalities,obtaining the fused audio-video modality features. Next, a causal gating cross-modal fusion network is used to fully integratethe heterogeneous data of text, audio, and video modalities. Finally, the sentiment analysis results are classified using SoftMax.The proposed cross-modal fusion network demonstrated superior performance in sentiment classification when compared tobaseline techniques on the CMU-MOSEI dataset. It effectively associated and combined pertinent multi-modal information.

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“…In Eq (3), H m represents the output of the embedding layer. The parameters of the embedding layer are learnable, with the purpose of mapping input vectors to a high-dimensional space.…”

Section: Feature Masking Text Embedding Module (Ft)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A multimodal sentiment model based on causal gating attention mechanism

Gan,

Sun,

Liu

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Some of the survey papers focus on only one direction or angle of sentiment analysis. Multimodal sentiment analysis and its applications, as well as its prospects, challenges, and adjacent fields, were the main topics of the paper by Kaur and Kautish (2022). Schouten and Frasincar…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis: A survey on design framework, applications and future scopes

Bordoloi

Biswas

2023

Artif Intell Rev

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Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

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“…Online videos have become the predominant data type that users use to share their activities and interact with each other in cyberspace. Inevitably, the scientific and industrial communities have given much attention to video data analysis, especially in video sentiment analysis and emotion recognition [1], [2] The associate editor coordinating the review of this manuscript and approving it for publication was Muhammad Asif .…”

Section: Introductionmentioning

confidence: 99%