AI Research Analysis
Multimodal Emotion Analysis of Online Product Marketing Information in Social Interaction and Internet Behavior Based on Generative Artificial Intelligence
With the proliferation of multimodal data such as text, images and videos on social networks, it is particularly important to extract valuable information from these diverse data sources. In this study, a multimodal sentiment analysis framework based on generative artificial intelligence (AI) technology is proposed, aiming at automatically identifying the emotional tendency in online product marketing information. By combining the latest generative AI technology, the model can not only analyze the direct feedback from users, but also capture the potential emotional color.
Executive Impact
The experimental results demonstrate a significant uplift in performance, offering concrete benefits for enterprises leveraging multimodal data.
0%
Accuracy Improvement
0%
Recall Rate Increase
0
Overall Model Accuracy
The demonstrated multimodal sentiment analysis framework provides a powerful tool for understanding and responding to consumer sentiment. Future research will further explore how to enhance the generalization ability of the model and how to deal with emotional differences in cross-cultural background more effectively to achieve a wider application in the global scope.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Research Background & Literature Review
In recent years, with the rapid development of Internet technology and the wide application of social media platforms, consumers' online behavior has changed significantly. Social media platforms have become the main channel for consumers to share their shopping experiences and express their personal opinions. These user-generated contents (UGCs) include words, pictures, audio, and video, forming so-called multimodal data. Enterprises are leveraging these platforms to promote products and services, recognizing their value in enhancing brand awareness and understanding consumer needs. However, extracting valuable information from massive multimodal data poses a significant challenge.
Traditional single-modal sentiment analysis often fails to capture complex emotional expressions, as many emotional clues are conveyed through non-text forms. Multimodal sentiment analysis aims to combine diverse content types for more accurate emotion readings. Recent advancements in generative AI are enhancing the ability to identify user emotional states and generate conforming response content, showing potential in customer service and customized marketing.
The study aims to develop a comprehensive multimodal sentiment analysis system that automatically detects and evaluates emotional tendencies in online product marketing information. Objectives include building a model for text and visual data, exploring modal interaction, evaluating performance on actual datasets, and analyzing its practical advantages and limitations.
Model Architecture & Components
The proposed sentiment analysis tool is designed to be compatible with multimodal data, automatically identifying and classifying sentiment types in social media data. It adopts an end-to-end architecture comprising three major modules: text processing, image processing, and fusion.
The text processing module utilizes the Transformer architecture, specifically an encoder module, to refine semantic features from long sequence texts. Key components include the multi-head attention mechanism and feedforward neural network, enhanced by position encoding.
For image processing, a Convolutional Neural Network (CNN) is used to extract local features and reduce parameters. A Global Average Pooling (GAP) layer compresses the feature map into a fixed-length vector. The fusion module then combines these text and image features into a unified representation using concatenation and a nonlinear transformation via a fully connected layer, with a softmax layer predicting emotion categories.
Experimental Design & Performance
The model was trained using a cross-entropy loss function and minimized with random gradient descent. Performance was evaluated using Accuracy, Precision, Recall, and F1-score. A high-quality dataset of product review data from platforms like Weibo, TikTok, Xiaohongshu, Taobao/JD.com, and Zhihu was collected, ensuring diversity in product categories, emotional tendencies (positive, negative, neutral), and expression habits.
Data preprocessing involved duplicate removal, cleaning (HTML tags, special characters), format conversion, and professional emotional labeling. The experimental environment included a high-performance GPU, Python, and DL frameworks like TensorFlow and PyTorch. Parameters such as learning rate (0.001), batch size (64), and dropout rate (0.1-0.5) were carefully tuned.
The model achieved an accuracy of 0.88, precision of 0.90, recall of 0.86, and an F1 score of 0.88, outperforming other models like MResNet, FNA, H-LSTM, and TMSA. It completed training in 4.5 hours with 12.3 million parameters, demonstrating superior efficiency. Performance across data types was 0.89 for text, 0.87 for images, and 0.85 for video, highlighting its robustness in complex multimodal scenarios.
Conclusion & Future Work
The proposed multimodal sentiment analysis method represents a significant breakthrough in interpreting social media users' emotional orientation. It effectively captures nuanced emotional details from diverse data types (text, images, sounds) and explores hidden connections between them. This approach offers substantial application significance in areas like customer service, market research, and public opinion monitoring, by providing accurate insights into industry trends, consumer demands, and real-time sentiment variations, supporting strategic formulation.
While making progress, the study acknowledges limitations such as weak adaptability to special data types and reliance on large training datasets. Future work will focus on optimizing model structure for enhanced universality and anti-interference, expanding data scope, improving decision visibility, and developing cross-language processing paths. Efforts will also be made to integrate the system into existing business chains for automated emotion tracking and feedback loops.
Addressing challenges like safeguarding personal privacy data and optimizing computing power for high-end NLP tools remains crucial for wider applicability and resource efficiency.
Key Research Insights
Overall Model Accuracy
0 The proposed model achieved superior classification accuracy across diverse datasets.Text Data Performance
0 The model demonstrates excellent performance when processing plain text sentiment.Training Efficiency
0 The new model significantly reduces training time compared to other models.Enterprise Process Flow
Text Data Input
→
Text Processing (Transformer)
→
Semantic Feature Extraction
→
Image Data Input
→
Image Processing (CNN)
→
Visual Feature Extraction
→
Feature Fusion
→
Sentiment Classification
| Model | F1-Score | Precision | Recall Rate | Accuracy Rate |
|---|---|---|---|---|
| Model O (Proposed) | 0.88 | 0.90 | 0.86 | 0.88 |
| Model A (MResNet) | 0.78 | 0.79 | 0.77 | 0.78 |
| Model B (FNA) | 0.79 | 0.80 | 0.78 | 0.79 |
| Model C (H-LSTM) | 0.81 | 0.82 | 0.80 | 0.81 |
| Model D (TMSA) | 0.80 | 0.81 | 0.79 | 0.80 |
Comparison of the proposed model (Model O) against other state-of-the-art multimodal sentiment analysis models, based on Figure 3, highlighting its superior performance across key metrics.
Enhanced Market Insight & Strategy
The multimodal sentiment analysis provides deeper market insights, enabling enterprises to better understand customer needs and optimize marketing strategies.
- Improved Product Design: By analyzing user-generated content (comments, pictures), enterprises can better understand consumer preferences during product development.
- Targeted Marketing: Accurate sentiment analysis helps formulate more targeted marketing strategies based on consumer emotional tendencies.
- Real-time Trend Monitoring: Grasping public opinion trends and sentiment variations in real-time allows for proactive strategy adjustments.
Revolutionizing Customer Service
Implementing this AI model transforms customer service systems, making them more empathetic and efficient.
- Proactive Problem Identification: Multimodal emotional analysis of customer feedback enables early detection of potential issues.
- Natural Interaction: The system accurately grasps user intentions and generates natural, relevant replies, improving user satisfaction.
- Enhanced Service Quality: The model's ability to understand subtle emotional changes contributes to a higher level of service quality and functional extension.
Calculate Your Potential ROI
See how implementing advanced AI for multimodal sentiment analysis can translate into tangible business savings and efficiency gains for your organization.
Projected Annual Savings
$0
Employee Hours Reclaimed Annually
0
Your AI Implementation Roadmap
A structured approach to integrating multimodal sentiment analysis into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Discovery & Strategy (1-2 Weeks)
Comprehensive assessment of your current data landscape, business objectives, and existing sentiment analysis capabilities. Define key performance indicators (KPIs) and a tailored AI strategy.
Phase 2: Data Preparation & Model Customization (4-6 Weeks)
Assist in multimodal data collection, cleaning, and labeling. Fine-tune the generative AI model to your specific industry nuances and emotional contexts for optimal accuracy.
Phase 3: Integration & Deployment (3-5 Weeks)
Seamless integration of the AI sentiment analysis framework into your existing social media monitoring tools, CRM, and customer service platforms. Deploy the model securely within your infrastructure.
Phase 4: Monitoring, Optimization & Training (Ongoing)
Continuous monitoring of model performance, A/B testing, and iterative refinements. Provide training for your teams to effectively leverage AI-driven insights for decision-making and strategy.
Ready to Transform Your Marketing Insights?
Unlock deeper customer understanding and refine your strategies with cutting-edge multimodal AI. Let's discuss how our solution can be tailored for your enterprise.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat