Sentiment Analysis of Fashion Related Posts in Social Media

TL;DR

This paper introduces a novel multimodal framework for sentiment analysis of fashion-related social media posts, integrating images, text, and fashion attributes to improve prediction accuracy, supported by a new large-scale dataset.

Contribution

The work presents a new model that combines fashion attributes with visual and textual data using mutual attention, addressing limitations of previous methods.

Findings

The proposed model outperforms existing approaches in sentiment classification accuracy.

A large-scale dataset of 12,000+ fashion social media posts was created for this task.

Extensive experiments validate the effectiveness of the multimodal and attribute integration approach.

Abstract

The role of social media in fashion industry has been blooming as the years have continued on. In this work, we investigate sentiment analysis for fashion related posts in social media platforms. There are two main challenges of this task. On the first place, information of different modalities must be jointly considered to make the final predictions. On the second place, some unique fashion related attributes should be taken into account. While most existing works focus on traditional multimodal sentiment analysis, they always fail to exploit the fashion related attributes in this task. We propose a novel framework that jointly leverages the image vision, post text, as well as fashion attribute modality to determine the sentiment category. One characteristic of our model is that it extracts fashion attributes and integrates them with the image vision information for effective representation. Furthermore, it exploits the mutual relationship between the fashion attributes and the post texts via a mutual attention mechanism. Since there is no existing dataset suitable for this task, we prepare a large-scale sentiment analysis dataset of over 12k fashion related social media posts. Extensive experiments are conducted to demonstrate the effectiveness of our model.