Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (1806.06228v1)

Abstract: Multimodal sentiment analysis is a very actively growing field of research. A promising area of opportunity in this field is to improve the multimodal fusion mechanism. We present a novel feature fusion strategy that proceeds in a hierarchical fashion, first fusing the modalities two in two and only then fusing all three modalities. On multimodal sentiment analysis of individual utterances, our strategy outperforms conventional concatenation of features by 1%, which amounts to 5% reduction in error rate. On utterance-level multimodal sentiment analysis of multi-utterance video clips, for which current state-of-the-art techniques incorporate contextual information from other utterances of the same clip, our hierarchical fusion gives up to 2.4% (almost 10% error rate reduction) over currently used concatenation. The implementation of our method is publicly available in the form of open-source code.

Multimodal Sentiment Analysis Using Hierarchical Fusion with Context Modeling

The paper "Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling" presents a nuanced approach to the emerging field of multimodal sentiment analysis, emphasizing the need for improved fusion mechanisms that consider contextual dependencies among modalities. The authors introduce a hierarchical strategy for feature fusion that is shown to outperform traditional concatenation methods, achieving up to a 2.4% increase in classification accuracy and a nearly 10% error rate reduction in dealing with sentiment analysis tasks.

Overview of the Methodology

The paper discusses a pioneering hierarchical feature fusion scheme that proceeds through several stages. Initially, the paper introduces unimodal feature extraction followed by bimodal fusion and finally trimodal fusion. These stages are designed to refine modality information progressively, leveraging Gated Recurrent Units (GRUs) to incorporate contextual information between utterances.

Unimodal Feature Extraction

The process begins with the extraction of unimodal features from three data streams: text, audio, and video. Textual features are derived using Convolutional Neural Networks (CNNs) with pretrained word embeddings. Audio features utilize openSMILE for detailed low-level descriptor extraction, and visual features are extracted using 3D-CNNs capable of capturing temporal dynamics in video data.

Hierarchical Fusion Process

The core innovation of the paper is its hierarchical fusion mechanism. This methodology addresses the shortcomings of early fusion—specifically its inability to omit conflicting or redundant information across modalities. The proposed hierarchical model fuses features first at a bimodal level (considering pairs of modalities like text-audio, text-video, etc.) and then integrates these bimodal vectors into a comprehensive trimodal vector.

Contextual Modeling

Enhancing the feature vectors' utility, the authors incorporate long-range contextual dependencies using GRUs. This allows the model to leverage surrounding utterances for improved sentiment prediction accuracy.

Experimental Results and Performance

The paper utilizes datasets such as CMU-MOSI and IEMOCAP, widely recognized benchmarks for multimodal sentiment analysis. The proposed hierarchical fusion model outperforms state-of-the-art techniques by a noticeable margin, particularly in configurations where textual data significantly influences the sentiment classification's effectiveness. Notably, this approach achieves an accuracy of 80% on the CMU-MOSI dataset for trimodal combinations, marking an improvement over the current methodologies.

Implications and Future Work

The hierarchical fusion model proposed in this paper offers a substantial leap forward in utilizing multimodal data for sentiment analysis, emphasizing the importance of context in interpreting sentiment across different modalities. The paper suggests a future focus on enhancing unimodal feature quality, with special attention to textual features, to further refine sentiment classification models. Additionally, exploring more advanced network architectures could provide further gains in performance.

Given the growing importance of sentiment analysis in applications ranging from social media monitoring to automated customer feedback systems, this paper's contributions mark a critical step in developing robust and context-sensitive analytical models. As researchers continue to build upon this work, we can anticipate more sophisticated, accurate, and nuanced multimodal sentiment analysis tools in various practical and theoretical applications.