Multimodal Sentiment Analysis Research Topics

Multimodal Sentiment Analysis Research Topics is used in this research. In this research we have to investigate and understand the expression on the basis of sentiments. To handle some issues in the existing research we proposed this research to overcome it. Below we provide the details about this proposed Multimodal Sentiment Analysis.

  1. Define Multimodal Sentiment Analysis

In this research we initially begin with the definition of our proposed research. It is the procedure of understanding and examining multiple approaches like audio, text, videos and images, to obtain a comprehensive interpretation of attitudes and emotions carried out in interaction.

  1. What is Multimodal Sentiment Analysis?

Then afterwards we look over the in-depth explanation for this proposed technology. It combines the techniques from computer vision, natural language processing and audio processing to take the various expressions of sentiment. It is the study of interpreting and investigating attitudes and emotions expressed in the interaction over different approaches like video, images, text and video.

  1. Where Multimodal Sentiment Analysis used?

Afterwards the in-depth explanation we converse about where to use this proposed technology. It identifies the applications in different fields like for interpreting public opinion by social media monitoring and for identifying customer feedback among audio, video modalities, images, aiding in decision making, strategy formulation and text by market research.

  1. Why Multimodal Sentiment Analysis technology proposed? , previous technology issues

Here we offer an early prediction of sentiment examination with emotions classification. The goal is to process the innovative multimodal sentiment analysis, allowing its real world usage in real-time situations like customer feedback examining, brand sentiment analysis validation and the social media monitoring. Some of the existing technology issues are Low Classification Rate for sentiment, A Deficit of Storage, Emotional Skill, and Restrictions Must Be Followed in Sarcastic Remarks.

  1. Algorithms / protocols

Our proposed Multimodal Sentiment Analysis technology is proposed to overcome the issues in the existing technology. The methods that we utilized are Multi-Class CatBoost Algorithm, Attention-based Deep GRU, Soft Actor Critic (SAC) and SpatioTemporal Optics (STO) Algorithm.

  1. Comparative study / Analysis

The comparative analysis section compares various methods related to this proposed sentiment analysis and to gain the best outcome for this research. The methods that we compared are as follows:

  • Detailed examination of model-level fusion to find the multimodal model which integrates visual and auditory approaches for emotion finding. More unique, accurately, innovative feature extractor networks for audio and the video data.
  • Motivated by the emotional arousal model in cognitive science, a Deep Emotional Arousal Network (DEAN) which is able to pretend emotional coherence.
  • To discover the link among emotional picture and text regions for multimodal sentiment analysis, a novel image-text interaction network (ITIN) has been generated.
  • By utilizing an ensemble transfer learning technique, a hybrid MSA model on the basis of Weighted Convolutional Neural Network is proposed. The recommended technique in this work will moreover generate the utilization of the extended Dempster-Shafer (Yager) theory to combine the outputs of the picture and text categorization to determine the final difference at the decision level.
  • The creative Multimodal Factorized Bilinear pooling (MFB) and Heterogeneous Convolutional Neural Networks (HCCNs) deep learning model for emotion findings.
  1. Simulation results / Parameters

For this research we compared different metrics or parameters to get the best finding for this research. The metrics that we compared are Computation Time, Recall, F-score, Precision and Accuracy and then in Accuracy it is classified as three categories namely Overall Accuracy, Sentiment Classification Accuracy and Emotion Classification Accuracy.

  1. Dataset LINKS / Important URL

The above we mentioned are the links that are provided to overview the doubts or any clarifications that are relevant to our proposed concept Multimodal Sentiment Analysis.

  1. Multimodal Sentiment Analysis Applications

There are numerous applications using this proposed technique. One of the examples for multimodal sentiment analysis application is in social media monitoring platforms, where the grouping of video, text and images is examined to measure the public sentiment on the way to events, brands or products. This assists business interpretation and reply to customer opinion efficiently, controlling their marketing policies and brand management efforts.

  1. Topology for Multimodal Sentiment Analysis

Data Collection, Data Preprocessing, Modality-Specific Analysis, Feature Fusion, Multimodal Fusion, Sentiment Analysis Model, Evaluation and Validation, Deployment and Integration and Continuous Enhancement these are the some of the topologies that to be utilized by our proposed approach.

  1. Environment for Multimodal Sentiment Analysis

Environment that to be utilized by our proposed approaches are as follows: Data Storage and Management, Model Deployment and Serving, Development Environment and Hardware Software are some of the environments for this sentiment analysis technique.

  1. Simulation tools

The proposed sentiment analysis technique utilizes the succeeding software requirements to be implemented in this proposed technology. Our proposed technique is simulated by using the developmental tool Python – 3.11.4 or Above version. Then the proposed sentiment approach can be executed by using the operating system Windows 10 (64-bit).

  1. Results

We proposed a multimodal sentiment analysis approach in this work and it overcomes several previous technology issues. The proposed technique metrics are compared with the various existing technologies and it verifies that our proposed techniques give the best findings with high accuracy. The proposed technique is implemented by using the operating system Windows 10 (64-bit).

Multimodal Sentiment Analysis Research Ideas:

Succeeding are the research topics on the basis of our proposed approach Multimodal Sentiment analysis, these topics give assistance to us when we have to clarify the doubts relevant to this proposed technique.

  1. HMAI-BERT: Hierarchical Multimodal Alignment and Interaction Network-Enhanced BERT for Multimodal Sentiment Analysis
  2. Hierarchical Interactive Multimodal Transformer for Aspect-Based Multimodal Sentiment Analysis
  3. Multimodal Learning with Incompleteness towards Multimodal Sentiment Analysis and Emotion Recognition Task
  4. Targeted Aspect-Based Multimodal Sentiment Analysis: An Attention Capsule Extraction and Multi-Head Fusion Network
  5. A Novel Multimodal Sentiment Analysis Model Based on Gated Fusion and Multi-Task Learning
  6. UniMF: A Unified Multimodal Framework for Multimodal Sentiment Analysis in Missing Modalities and Unaligned Multimodal Sequences
  7. The Weighted Cross-Modal Attention Mechanism With Sentiment Prediction Auxiliary Task for Multimodal Sentiment Analysis
  8. An Interactive Attention Mechanism Fusion Network for Aspect-Based Multimodal Sentiment Analysis
  9. Research on Microblog Sentiment Analysis Based on Multimodal and Multiscale Feature Fusion
  10. A Unimodal Reinforced Transformer With Time Squeeze Fusion for Multimodal Sentiment Analysis
  11. Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis
  12. Multi-Channel Attentive Graph Convolutional Network with Sentiment Fusion for Multimodal Sentiment Analysis
  13. CLIP-MSA: Incorporating Inter-Modal Dynamics and Common Knowledge to Multimodal Sentiment Analysis With Clip
  14. TensorFormer: A Tensor-Based Multimodal Transformer for Multimodal Sentiment Analysis and Depression Detection
  15. M3SA: Multimodal Sentiment Analysis Based on Multi-Scale Feature Extraction and Multi-Task Learning
  16. Fusion with GCN and SE-ResNeXt Network for Aspect Based Multimodal Sentiment Analysis
  17. MEDT: Using Multimodal Encoding-Decoding Network as in Transformer for Multimodal Sentiment Analysis
  18. Exploring Multimodal Sentiment Analysis through Cartesian Product approach using BERT Embeddings and ResNet-50 encodings and comparing performance with pre-existing models
  19. Arabic language investigation in the context of unimodal and multimodal sentiment analysis
  20. Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms
  21. Multimodal Sentiment Analysis Based on Information Bottleneck and Attention Mechanisms
  22. AdaMoW: Multimodal Sentiment Analysis Based on Adaptive Modality-Specific Weight Fusion Network
  23. Multimodal Sentiment Analysis under modality deficiency with prototype-Augmentation in software engineering
  24. Multimodal Sentiment Analysis Based on Attention Mechanism and Tensor Fusion Network
  25. Capturing High-Level Semantic Correlations via Graph for Multimodal Sentiment Analysis
  26. Multimodal Sentiment Analysis Based on Nonverbal Representation Optimization Network and Contrastive Interaction Learning
  27. DFNM: Dynamic Fusion Network of Intra- and Inter-modalities for Multimodal Sentiment Analysis
  28. Multimodal Sentiment Analysis: Techniques, Implementations and Challenges across Diverse Modalities
  29. Multimodal Sentiment Analysis Missing Modality Reconstruction Network Based on Shared-Specific Features
  30. Multimodal Sentiment Analysis Based on Attentional Temporal Convolutional Network and Multi-Layer Feature Fusion
  31. Multimodal Sentiment Analysis based on Supervised Contrastive Learning and Cross-modal Translation under Modalities Missing
  32. Efficient Multimodal Transformer With Dual-Level Feature Restoration for Robust Multimodal Sentiment Analysis
  33. Boosting Modality Representation With Pre-Trained Models and Multi-Task Training for Multimodal Sentiment Analysis
  34. Attention-Based Fusion of Intra- and Intermodal Dynamics in Multimodal Sentiment Analysis
  35. The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
  36. The Multimodal Sentiment Analysis in Car Reviews (MuSe-CaR) Dataset: Collection, Insights and Improvements
  37. Multimodal sentiment analysis based on multi-head self-attention and convolutional block attention module
  38. Dynamically Shifting Multimodal Representations via Hybrid-Modal Attention for Multimodal Sentiment Analysis
  39. Hybrid Contrastive Learning of Tri-Modal Representation for Multimodal Sentiment Analysis
  40. Inter-Intra Modal Representation Augmentation With Trimodal Collaborative Disentanglement Network for Multimodal Sentiment Analysis
  41. HEROCA: Multimodal Sentiment Analysis Based on HEterogeneous Representation Optimization and Cross-Modal Attention
  42. On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for Multimodal Sentiment Analysis
  43. Multimodal Sentiment Analysis with Preferential Fusion and Distance-aware Contrastive Learning
  44. MMATR: A Lightweight Approach for Multimodal Sentiment Analysis Based on Tensor Methods
  45. Improving the Modality Representation with multi-view Contrastive Learning for Multimodal Sentiment Analysis
  46. A Multi-Stage Hierarchical Relational Graph Neural Network for Multimodal Sentiment Analysis
  47. Dominant SIngle-Modal SUpplementary Fusion (SIMSUF) For Multimodal Sentiment Analysis
  48. Multichannel Cross-Modal Fusion Network for Multimodal Sentiment Analysis Considering Language Information Enhancement
  49. FMSA-SC: A Fine-Grained Multimodal Sentiment Analysis Dataset Based on Stock Comment Videos
  50. Progress, achievements, and challenges in multimodal sentiment analysis using deep learning: A survey