Multimodal Sentiment Analysis To Explore the Structure of Emotions (1805.10205v1)

Abstract: We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the latent emotional state of the user. Thus, we focus on predicting the emotion word tags attached by users to their Tumblr posts, treating these as "self-reported emotions." We demonstrate that our multimodal model combining both text and image features outperforms separate models based solely on either images or text. Our model's results are interpretable, automatically yielding sensible word lists associated with emotions. We explore the structure of emotions implied by our model and compare it to what has been posited in the psychology literature, and validate our model on a set of images that have been used in psychology studies. Finally, our work also provides a useful tool for the growing academic study of images - both photographs and memes - on social networks.

• The paper introduces a multimodal deep neural network that fuses image and text data to infer nuanced emotional states.
• It leverages an Inception CNN for image processing and GloVe embeddings with RNNs for textual analysis to capture sentiment cues.
• Empirical results show a 72% test accuracy, demonstrating the advantage of integrating modalities over unimodal approaches.

Analyzing the Integration of Multimodal Data in Sentiment Analysis

The paper "Multimodal Sentiment Analysis to Explore the Structure of Emotions" presents a comprehensive approach to sentiment analysis by employing multimodal deep neural networks that integrate visual and textual data. Unlike traditional sentiment analysis that predominantly classifies text into positive or negative sentiment, this work aims to infer the latent emotional states of users as expressed through their Tumblr posts. In this paper, the authors have leveraged emotion word tags included by users as indicators of self-reported emotions and endeavored to develop a model that outperforms unimodal approaches by combining image and text features.

Key Methodological Components

The core of the proposed framework involves several crucial methodologies:

• Image Processing: The authors utilized the Inception deep convolutional neural network, pre-trained on the ImageNet dataset, to fine-tune it for emotion inference. By doing this, the deep architecture harnesses the complexity of image representation, being sensitive to different levels of abstraction necessary for understanding image sentiment.
• Textual Analysis: For textual sentiment, the paper implements GloVe word embeddings to transform words into high-dimensional vectors that preserve semantic similarity. These embeddings are then processed via a recurrent neural network that captures the sequential nature of language, instrumental in grasping nuances in human communication.
• Multimodal Fusion: The authors propose an integrated network, termed "Deep Sentiment," which combines both the visual and textual modalities at a dense layer. This fusion is followed by a softmax layer that provides a probability distribution over the possible emotion tags.

Empirical Insights and Model Evaluation

Empirical results indicate that the proposed multimodal model achieves a test accuracy of 72%, surpassing the performance of models relying solely on images or text. By comparison, the image and text-only models attained accuracies of 36% and 69%, respectively, underscoring the advantage of integrating different data forms. The authors substantiate the psychological credibility of their results, aligning inferred emotions with established emotional clusters from psychology literature.

The model's ability to identify salient words for each emotion offers an innovative, data-driven alternative to classical lexicon-based approaches such as LIWC, demonstrating its adeptness in handling contemporary language nuances prevalent in social media.

Implications and Future Directions

From a theoretical perspective, this research contributes to the burgeoning field of affective computing by providing a methodological framework for decoding human emotions through multimodal data. On a practical level, it presents viable approaches for social scientists to investigate emotional expressions in online platforms where text and images are intertwined.

Speculatively, the future of AI in sentiment analysis could see further refinement in the integration of more diverse data sources, including audio and physiological signals, to enhance the accuracy of emotion inference. Moreover, ethical considerations surrounding data privacy and the representation biases in emotion datasets will likely become pressing topics as this domain progresses.

In summary, this research offers a robust multimodal approach to sentiment analysis, making valuable strides towards a more granular understanding of emotional expressions beyond binary sentiment classification. The application of advanced neural architectures to unify visual and textual data paves the way for explorations into more generalized models of human emotion recognition.