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PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment

Shezheng Song, Shasha Li, Shan Zhao, Chengyu Wang, Xiaopeng Li, Jie Yu, Qian Wan, Jun Ma, Tianwei Yan, Wentao Ma, Xiaoguang Mao

TL;DR

The paper tackles multimodal aspect-based sentiment analysis by challenging the prevailing joint-prediction paradigm and proposing a pipeline framework that first extracts aspects (MATE) and then classifies sentiment (MASC) with translation-based alignment (TBA). It introduces a semantic-consistency objective that aligns vision and text through a cross-modal translation, yielding state-of-the-art results on Twitter-15 and Twitter-17 datasets and outperforming large language models on MABSA tasks. The authors provide thorough ablations showing the distinct feature requirements of MATE and MASC, the crucial role of predicted aspects for image utilization, and the benefits of using translation-based alignment to mitigate noise in social-media images. The work contributes practical guidelines for future MABSA research, demonstrates robust performance without heavy pretraining, and offers reproducibility through code release.

Abstract

Multimodal aspect-based sentiment analysis (MABSA) aims to understand opinions in a granular manner, advancing human-computer interaction and other fields. Traditionally, MABSA methods use a joint prediction approach to identify aspects and sentiments simultaneously. However, we argue that joint models are not always superior. Our analysis shows that joint models struggle to align relevant text tokens with image patches, leading to misalignment and ineffective image utilization. In contrast, a pipeline framework first identifies aspects through MATE (Multimodal Aspect Term Extraction) and then aligns these aspects with image patches for sentiment classification (MASC: Multimodal Aspect-Oriented Sentiment Classification). This method is better suited for multimodal scenarios where effective image use is crucial. We present three key observations: (a) MATE and MASC have different feature requirements, with MATE focusing on token-level features and MASC on sequence-level features; (b) the aspect identified by MATE is crucial for effective image utilization; and (c) images play a trivial role in previous MABSA methods due to high noise. Based on these observations, we propose a pipeline framework that first predicts the aspect and then uses translation-based alignment (TBA) to enhance multimodal semantic consistency for better image utilization. Our method achieves state-of-the-art (SOTA) performance on widely used MABSA datasets Twitter-15 and Twitter-17. This demonstrates the effectiveness of the pipeline approach and its potential to provide valuable insights for future MABSA research. For reproducibility, the code and checkpoint will be released.

PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment

TL;DR

The paper tackles multimodal aspect-based sentiment analysis by challenging the prevailing joint-prediction paradigm and proposing a pipeline framework that first extracts aspects (MATE) and then classifies sentiment (MASC) with translation-based alignment (TBA). It introduces a semantic-consistency objective that aligns vision and text through a cross-modal translation, yielding state-of-the-art results on Twitter-15 and Twitter-17 datasets and outperforming large language models on MABSA tasks. The authors provide thorough ablations showing the distinct feature requirements of MATE and MASC, the crucial role of predicted aspects for image utilization, and the benefits of using translation-based alignment to mitigate noise in social-media images. The work contributes practical guidelines for future MABSA research, demonstrates robust performance without heavy pretraining, and offers reproducibility through code release.

Abstract

Multimodal aspect-based sentiment analysis (MABSA) aims to understand opinions in a granular manner, advancing human-computer interaction and other fields. Traditionally, MABSA methods use a joint prediction approach to identify aspects and sentiments simultaneously. However, we argue that joint models are not always superior. Our analysis shows that joint models struggle to align relevant text tokens with image patches, leading to misalignment and ineffective image utilization. In contrast, a pipeline framework first identifies aspects through MATE (Multimodal Aspect Term Extraction) and then aligns these aspects with image patches for sentiment classification (MASC: Multimodal Aspect-Oriented Sentiment Classification). This method is better suited for multimodal scenarios where effective image use is crucial. We present three key observations: (a) MATE and MASC have different feature requirements, with MATE focusing on token-level features and MASC on sequence-level features; (b) the aspect identified by MATE is crucial for effective image utilization; and (c) images play a trivial role in previous MABSA methods due to high noise. Based on these observations, we propose a pipeline framework that first predicts the aspect and then uses translation-based alignment (TBA) to enhance multimodal semantic consistency for better image utilization. Our method achieves state-of-the-art (SOTA) performance on widely used MABSA datasets Twitter-15 and Twitter-17. This demonstrates the effectiveness of the pipeline approach and its potential to provide valuable insights for future MABSA research. For reproducibility, the code and checkpoint will be released.
Paper Structure (22 sections, 12 equations, 4 figures, 7 tables, 2 algorithms)

This paper contains 22 sections, 12 equations, 4 figures, 7 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Pipeline Framework
  4. Aspect Extraction
  5. Sentiment Classification
  6. Translation-Based Alignment
  7. Training Objective
  8. Experimental Setting
  9. Datasets, Metrics and Implement Details
  10. Baseline
  11. Results and Analysis
  12. MABSA Results
  13. MATE and MASC Results
  14. Ablation Study
  15. The Experiment Supporting Our Observations and Analyses
  16. ...and 7 more sections

Figures (4)

  • Figure 1: Frameworks of MABSA. "B-pos" means "Begin, Positive"; I and O represent "In" and "Out" respectively, indicating the aspect position. In pipeline framework, MATE first identifies the aspect as "Messi." Then, "Messi" interacts with the image for sentiment classification "Positive."
  • Figure 2: Overview of our method. AE means aspect extraction module, which is used to extract "Messi" from the sentence for the subsequent sentiment classification task. $a$ and $t$ represent the aspect and sentence feature, respectively. $a'$ is the translated image feature, aimed at maintaining semantic consistency with $a$. $B$ denotes the beginning of the aspect position, while $O$ indicates that the token is not an aspect.
  • Figure 3: Experimental analysis on Twitter15. (a) The performance of MATE and MASC with shared and not shared encoders. (b) The impact of selecting textual features: entire sentence or predicted aspect, to align with image. (c) F1 result of different methods with and w/o image.
  • Figure 4: Experimental analysis of hyperparameters on the Twitter15.