Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks (1509.06041v1)

Abstract: Sentiment analysis of online user generated content is important for many social media analytics tasks. Researchers have largely relied on textual sentiment analysis to develop systems to predict political elections, measure economic indicators, and so on. Recently, social media users are increasingly using images and videos to express their opinions and share their experiences. Sentiment analysis of such large scale visual content can help better extract user sentiments toward events or topics, such as those in image tweets, so that prediction of sentiment from visual content is complementary to textual sentiment analysis. Motivated by the needs in leveraging large scale yet noisy training data to solve the extremely challenging problem of image sentiment analysis, we employ Convolutional Neural Networks (CNN). We first design a suitable CNN architecture for image sentiment analysis. We obtain half a million training samples by using a baseline sentiment algorithm to label Flickr images. To make use of such noisy machine labeled data, we employ a progressive strategy to fine-tune the deep network. Furthermore, we improve the performance on Twitter images by inducing domain transfer with a small number of manually labeled Twitter images. We have conducted extensive experiments on manually labeled Twitter images. The results show that the proposed CNN can achieve better performance in image sentiment analysis than competing algorithms.

• The paper introduces a progressive training strategy that iteratively refines the CNN to mitigate noisy Flickr labels.
• It employs domain transfer by fine-tuning on a manually labeled Twitter dataset, enhancing generalizability across social media.
• Results show superior precision, recall, and F1 scores, outperforming traditional mid-level feature models in image sentiment analysis.

Robust Image Sentiment Analysis Using Progressively Trained and Domain Transferred Deep Networks

Introduction

The research by Quanzeng You et al. introduces a novel approach to image sentiment analysis, leveraging deep learning through Progressive Convolutional Neural Networks (PCNN). This paper addresses the growing importance of analyzing visual content, particularly in social media, where users often express sentiments in images alongside text. The work departs from traditional reliance on textual sentiment analysis by integrating visual content as a complementary signal, thus enhancing predictive capabilities in applications like political elections and economic indicators.

Methodology

The authors propose a specifically designed CNN architecture tailored for image sentiment analysis. The architecture involves two convolutional layers combined with several fully connected layers aimed at predicting sentiment labels. Significantly, the model utilizes a half-million dataset of Flickr images, which, while being machine-labeled and noisy, are ideal for exploring scalable training strategies.

A progressive training strategy is employed to minimize the impact of the noisy labels. This is achieved by iteratively fine-tuning the network, selecting training samples based on their confidence scores to filter out unreliable data. Additionally, domain transfer is incorporated by fine-tuning the model with a smaller, manually labeled dataset sourced from Twitter, enhancing the generalizability of the results across different platforms and domains.

Results

The experimental evaluations highlight considerable performance improvements over baseline methods, which predominantly relied on predefined visual features or attributes. The PCNN architecture exhibits superior precision, recall, and F1 scores. When tested on the Twitter dataset, intentionally collected for validation, the PCNN maintained robust performance, indicating successful domain adaptation through its fine-tuning processes.

Noteworthy is the PCNN's ability to outperform models utilizing mid-level features by effectively learning more abstract representations that align closely with human perception of sentiment. This advancement illustrates the efficacy of employing large-scale weakly labeled datasets, coupled with domain adaptation strategies.

Implications and Future Directions

The implications of this work are significant for both the theoretical understanding and practical applications of sentiment analysis in multimedia. The paper highlights how deep learning can effectively handle more subjective and abstract tasks, presenting enhanced feature extraction capabilities over conventional methods. This adds a layer of robustness and adaptability not previously available in static feature-based systems.

Future progress in AI could involve further integration of multimodal data—combining visual and textual signals—to create more comprehensive models for sentiment analysis. The exploration of additional domains and the adaptation of similar deep learning frameworks could potentially lead to sentiment analysis systems that are more universally applicable across diverse user-generated content.

In conclusion, this paper contributes a refined approach to image sentiment analysis, effectively leveraging progressively trained and domain-transferred deep learning networks. It advances the field by demonstrating the potential and practicality of deep learning architectures to contextualize and analyze visual data at scale.