Enterprise AI Analysis
Incorporating Multimodal Commonsense and Heterogeneous User Knowledge for Personalized Implicit Sentiment Analysis in Chinese
This analysis presents MOHUK, a novel framework designed to enhance Implicit Sentiment Analysis (ISA) in Chinese by integrating multimodal commonsense and heterogeneous user knowledge. MOHUK addresses the subjectivity of implicit sentiments and the limitations of text-only models, providing a more nuanced and personalized understanding of user expressions.
Published: 09 March 2026 by Jian Liao et al. (Shanxi University)
Executive Impact Summary
The MOHUK model achieves significant performance improvements in personalized implicit sentiment analysis, demonstrating its capability to deliver more accurate and context-aware sentiment insights.
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F1-Macro Improvement (D-implicit)
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F1-Macro Improvement (D-general)
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Negative Sentiment F1 (D-general)
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Training Time
Deep Analysis & Enterprise Applications
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Personalized Implicit Sentiment Analysis (PISA)
Implicit Sentiment Analysis (ISA) is challenging due to the absence of explicit sentiment cues, making it highly sensitive to individual user characteristics. The paper introduces Personalized Implicit Sentiment Analysis (PISA) to address this subjectivity, recognizing that the same expression can evoke varying sentimental responses. MOHUK proposes a multi-stage knowledge integration pipeline to capture rich semantic representations, construct comprehensive user profiles, and enhance semantic understanding through multimodal commonsense.
Leveraging Multimodal Commonsense
MOHUK integrates multimodal commonsense knowledge by utilizing a Multimodal Large Language Model (MLLM) to generate semantic textual representations from images, thereby incorporating external knowledge. Raw images are also processed via a vision-encoder. This cross-modal semantic enhancement ensures a more comprehensive understanding of implicit sentiments, mitigating text-only limitations and addressing the challenge of insufficient sentimental information.
Heterogeneous User Knowledge Integration
The model constructs a cross-modal user-preference-correlated images interactive graph to capture diverse writing styles and habits, effectively modeling users' implicit preferences. It integrates user attributes, historical content, and implicit sentiment expressions, and employs graph neural networks for multi-view interaction learning, allowing for a personalized analysis of user sentiments.
Superior Performance and Practicality
MOHUK outperforms SOTA baselines and LLMs on two Chinese ISA datasets, with F1-macro improvements of 2.86% (D-implicit) and 3.03% (D-general). It particularly excels in negative sentiment recognition (F1-score 0.758 on D-general). Despite its advanced capabilities, MOHUK maintains practical efficiency, with end-to-end training requiring approximately 3.5 hours, comparable to other SOTA baselines.
0.612
F1-Macro Score on D-implicit Dataset (MOHUK)
Enterprise Process Flow
User Preference Aware Cross-modal Interaction Graph Learning
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User Heterogeneous Knowledge Encoding
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Cross-modal Commonsense Knowledge Enhancement
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Heterogeneous Knowledge Fusion and Sentiment Prediction
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Personalized Implicit Sentiment Prediction
| Model | F1-Macro D-Implicit | F1-Macro D-General |
|---|---|---|
| MOHUK | 0.612 | 0.680 |
| GLM4 | 0.586 | 0.648 |
| Qwen-14B(ft) | 0.585 | 0.589 |
| TOC | 0.595 | 0.623 |
Case 1: Accurate Negative Sentiment Prediction
Problem Statement: 边走湿透的裤子里面水直往鞋子里流! (The water inside the soaked pants kept flowing into the shoes as I walked!)
MOHUK Analysis: MOHUK accurately identified negative sentiment by integrating the user's historical content ('rain is really abnormal!') and commonsense knowledge (<rain, caused, wetness>, <rain, related_to, coldness>). These multimodal cues reinforced the inherent discomfort, leading to a correct 'Negative' classification.
Case 2: User Data Bias Leading to Misclassification (Limitation)
Problem Statement: 夏天来了,去年蝉蜕下的壳还会 在吗?(Summer is approaching soon, will last year's cicada shells still remain?)
MOHUK Analysis: The model incorrectly classified this as positive. The user's historical preferences ('pigeon', 'blue sky') were linked to positive commonsense (<pigeons, symbolize, peace>; <blue sky, related_to, cleanliness>), overriding the sequence's subtle melancholic tone. This highlights a limitation where strong positively biased external knowledge can overshadow the inherent sentiment.
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Estimated Annual Savings
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Hours Reclaimed Annually
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