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State of the art: a review of sentiment analysis based on sequential transfer learning
Recently, sequential transfer learning emerged as a modern technique for applying the
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
“pretrain then fine-tune” paradigm to leverage existing knowledge to improve the …
Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey
A Pandey, DK Vishwakarma - Applied Soft Computing, 2024 - Elsevier
Sentiment analysis is a computational technique that analyses the subjective information
conveyed within a given expression. This encompasses appraisals, opinions, attitudes or …
conveyed within a given expression. This encompasses appraisals, opinions, attitudes or …
Hybrid contrastive learning of tri-modal representation for multimodal sentiment analysis
The wide application of smart devices enables the availability of multimodal data, which can
be utilized in many tasks. In the field of multimodal sentiment analysis, most previous works …
be utilized in many tasks. In the field of multimodal sentiment analysis, most previous works …
Cross-modal prototype driven network for radiology report generation
Radiology report generation (RRG) aims to describe automatically a radiology image with
human-like language and could potentially support the work of radiologists, reducing the …
human-like language and could potentially support the work of radiologists, reducing the …
Multimodal information bottleneck: Learning minimal sufficient unimodal and multimodal representations
Learning effective joint embedding for cross-modal data has always been a focus in the field
of multimodal machine learning. We argue that during multimodal fusion, the generated …
of multimodal machine learning. We argue that during multimodal fusion, the generated …
Disentanglement translation network for multimodal sentiment analysis
Obtaining an effective joint representation has always been the goal for multimodal tasks.
However, distributional gap inevitably exists due to the heterogeneous nature of different …
However, distributional gap inevitably exists due to the heterogeneous nature of different …
SKEAFN: sentiment knowledge enhanced attention fusion network for multimodal sentiment analysis
Multimodal sentiment analysis is an active research field that aims to recognize the user's
sentiment information from multimodal data. The primary challenge in this field is to develop …
sentiment information from multimodal data. The primary challenge in this field is to develop …
BiSyn-GAT+: Bi-syntax aware graph attention network for aspect-based sentiment analysis
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims
to align aspects and corresponding sentiments for aspect-specific sentiment polarity …
to align aspects and corresponding sentiments for aspect-specific sentiment polarity …
Contrastive learning based modality-invariant feature acquisition for robust multimodal emotion recognition with missing modalities
Multimodal emotion recognition (MER) aims to understand the way that humans express
their emotions by exploring complementary information across modalities. However, it is …
their emotions by exploring complementary information across modalities. However, it is …
A comprehensive survey on deep learning-based approaches for multimodal sentiment analysis
Sentiment analysis is an important natural language processing issue that has many
applications in various fields. The increasing popularity of social networks and growth and …
applications in various fields. The increasing popularity of social networks and growth and …