Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning (1802.00924v1)

Abstract: With the increasing popularity of video sharing websites such as YouTube and Facebook, multimodal sentiment analysis has received increasing attention from the scientific community. Contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity, we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level. In this paper, we propose the Gated Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is composed of 2 modules. The Gated Multimodal Embedding alleviates the difficulties of fusion when there are noisy modalities. The LSTM with Temporal Attention performs word level fusion at a finer fusion resolution between input modalities and attends to the most important time steps. As a result, the GME-LSTM(A) is able to better model the multimodal structure of speech through time and perform better sentiment comprehension. We demonstrate the effectiveness of this approach on the publicly-available Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving state-of-the-art sentiment classification and regression results. Qualitative analysis on our model emphasizes the importance of the Temporal Attention Layer in sentiment prediction because the additional acoustic and visual modalities are noisy. We also demonstrate the effectiveness of the Gated Multimodal Embedding in selectively filtering these noisy modalities out. Our results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion.

Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement Learning: An Overview

The paper presents a novel approach towards multimodal sentiment analysis, focusing on the integration and enhancement of multiple data modalities—text, audio, and video—using a deep learning framework. This approach is particularly relevant in our current digital landscape, where platforms such as YouTube and Facebook are replete with user-generated content that demands sophisticated analysis techniques to understand inherent sentiments. The proposed model, referred to as Gated Multimodal Embedding with Temporal Attention Model (referred to often as simply the proposed model), aims to address the underlying complexities of fusing noisy multimodal data streams at a temporal resolution aligned with spoken words.

Methodological Advancements

Key elements of the proposed model include the following:

1. Word-Level Fusion: Departing from traditional mechanisms that rely heavily on video-level features, this approach meticulously aligns multimodal features at the word level. Such granularity allows for a refined capture of the interplay between spoken language, visual expression, and acoustic signals.
2. Gated Mechanism: The use of a Gated Multimodal Embedding addresses the issue of noise within non-verbal modalities. It selectively filters information, ensuring that only meaningful audio and visual inputs contribute to the prediction process. The gating is optimized using reinforcement learning, thus enabling the model to improve over iterations based on its performance outcomes.
3. Temporal Attention: An LSTM with Temporal Attention is deployed to allow the model to discern and focus on critical moments within the speech flow. This is crucial for accurately capturing sentiment that may be expressed succinctly through potent verbal and non-verbal cues.

Empirical Validations

The effectiveness of this model was empirically validated using the CMU-MOSI dataset, a comprehensive collection for multimodal sentiment analysis. Notably, the proposed model achieved state-of-the-art performance with substantial improvements in binary classification accuracy and mean absolute error (MAE) over existing multimodal models. The authors report a 3% improvement in accuracy and a reduction of MAE by 0.145 when compared to previous state-of-the-art models.

Implications and Future Directions

This research yields significant implications for both theoretical understanding and practical applications of sentiment analysis. Theoretically, it demonstrates the versatility of multimodal fusion at finer granularities and suggests potential upgrades to existing approaches that currently operate at a more abstract level. Practically, the model could influence the development of more perceptive human-computer interaction systems, capable of nuanced comprehension of user sentiments as they combine spoken content with emotional tones and facial expressions.

Looking forward, further developments may involve enhancing the robustness of modality alignment mechanisms and exploring the applications of this model to other tasks that benefit from understanding complex audiovisual significance, such as empathetic AI systems and advanced media analytics. Additionally, scaling the model for real-time processing and applying it to more diverse datasets would be instrumental in refining its efficacy and adaptability across different multimedia contexts.

Overall, this paper contributes to the growing body of research aiming to integrate machine learning techniques more deeply with multimedia understanding and presents a clear path for future exploration in an interdisciplinary domain at the intersection of artificial intelligence, computer vision, and natural language processing.