Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models

This project performs multimodal sentiment analysis using the CMU-MOSEI dataset, using transformer-based models with early fusion to integrate text, audio, and visual modalities. We employ BERT-based encoders for each modality, extracting embeddings that are concatenated before classification. The model achieves strong performance, with 97.87\% 7-class accuracy and a 0.9682 F1-score on the test set, demonstrating the effectiveness of early fusion in capturing cross-modal interactions. The training utilized Adam optimization (lr=1e-4), dropout (0.3), and early stopping to ensure generalization and robustness. Results highlight the superiority of transformer architectures in modeling multimodal sentiment, with a low MAE (0.1060) indicating precise sentiment intensity prediction. Future work may compare fusion strategies or enhance interpretability. This approach utilizes multimodal learning by effectively combining linguistic, acoustic, and visual cues for sentiment analysis.

Flower Across Time and Media: Sentiment Analysis of Tang Song Poetry and Visual Correspondence

The Tang (618 to 907) and Song (960 to 1279) dynasties witnessed an extraordinary flourishing of Chinese cultural expression, where floral motifs served as a dynamic medium for both poetic sentiment and artistic design. While previous scholarship has examined these domains independently, the systematic correlation between evolving literary emotions and visual culture remains underexplored. This study addresses that gap by employing BERT-based sentiment analysis to quantify emotional patterns in floral imagery across Tang Song poetry, then validating these patterns against contemporaneous developments in decorative arts.Our approach builds upon recent advances in computational humanities while remaining grounded in traditional sinological methods. By applying a fine tuned BERT model to analyze peony and plum blossom imagery in classical poetry, we detect measurable shifts in emotional connotations between the Tang and Song periods. These textual patterns are then cross berenced with visual evidence from textiles, ceramics, and other material culture, revealing previously unrecognized synergies between literary expression and artistic representation.

Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models

We explore the use of Chain-of-Thought (CoT) prompting with large language models (LLMs) to improve the accuracy of granular sentiment categorization in app store reviews. Traditional numeric and polarity-based ratings often fail to capture the nuanced sentiment embedded in user feedback. We evaluated the effectiveness of CoT prompting versus simple prompting on 2000 Amazon app reviews by comparing each method's predictions to human judgements. CoT prompting improved classification accuracy from 84% to 93% highlighting the benefit of explicit reasoning in enhancing sentiment analysis performance.

Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture

Multimodal sentiment analysis, a pivotal task in affective computing, seeks to understand human emotions by integrating cues from language, audio, and visual signals. While many recent approaches leverage complex attention mechanisms and hierarchical architectures, we propose a lightweight, yet effective fusion-based deep learning model tailored for utterance-level emotion classification. Using the benchmark IEMOCAP dataset, which includes aligned text, audio-derived numeric features, and visual descriptors, we design a modality-specific encoder using fully connected layers followed by dropout regularization. The modality-specific representations are then fused using simple concatenation and passed through a dense fusion layer to capture cross-modal interactions. This streamlined architecture avoids computational overhead while preserving performance, achieving a classification accuracy of 92% across six emotion categories. Our approach demonstrates that with careful feature engineering and modular design, simpler fusion strategies can outperform or match more complex models, particularly in resource-constrained environments.

Unraveling Media Perspectives: A Comprehensive Methodology Combining Large Language Models, Topic Modeling, Sentiment Analysis, and Ontology Learning to Analyse Media Bias

Biased news reporting poses a significant threat to informed decision-making and the functioning of democracies. This study introduces a novel methodology for scalable, minimally biased analysis of media bias in political news. The proposed approach examines event selection, labeling, word choice, and commission and omission biases across news sources by leveraging natural language processing techniques, including hierarchical topic modeling, sentiment analysis, and ontology learning with large language models. Through three case studies related to current political events, we demonstrate the methodology's effectiveness in identifying biases across news sources at various levels of granularity. This work represents a significant step towards scalable, minimally biased media bias analysis, laying the groundwork for tools to help news consumers navigate an increasingly complex media landscape.

Sentiment-Aware Recommendation Systems in E-Commerce: A Review from a Natural Language Processing Perspective

E-commerce platforms generate vast volumes of user feedback, such as star ratings, written reviews, and comments. However, most recommendation engines rely primarily on numerical scores, often overlooking the nuanced opinions embedded in free text. This paper comprehensively reviews sentiment-aware recommendation systems from a natural language processing perspective, covering advancements from 2023 to early 2025. It highlights the benefits of integrating sentiment analysis into e-commerce recommenders to enhance prediction accuracy and explainability through detailed opinion extraction. Our survey categorizes recent work into four main approaches: deep learning classifiers that combine sentiment embeddings with user item interactions, transformer based methods for nuanced feature extraction, graph neural networks that propagate sentiment signals, and conversational recommenders that adapt in real time to user feedback. We summarize model architectures and demonstrate how sentiment flows through recommendation pipelines, impacting dialogue-based suggestions. Key challenges include handling noisy or sarcastic text, dynamic user preferences, and bias mitigation. Finally, we outline research gaps and provide a roadmap for developing smarter, fairer, and more user-centric recommendation tools.

OODTE: A Differential Testing Engine for the ONNX Optimizer

With $700$ stars on GitHub and part of the official ONNX repository, the ONNX Optimizer consists of the standard method to apply graph-based optimizations on ONNX models. However, its ability to preserve model accuracy across optimizations, has not been rigorously explored. We propose OODTE, a utility to automatically and thoroughly assess the correctness of the ONNX Optimizer. OODTE follows a simple, yet effective differential testing and evaluation approach that can be easily adopted to other compiler optimizers. In particular, OODTE utilizes a number of ONNX models, then optimizes them and executes both the original and the optimized variants across a user-defined set of inputs, while automatically logging any issues with the optimization process. Finally, for successfully optimized models, OODTE compares the results, and, if any accuracy deviations are observed, it iteratively repeats the process for each pass of the ONNX Optimizer, to localize the root cause of the differences observed. Using OODTE, we sourced well-known $130$ models from the official ONNX Model Hub, used for a wide variety of tasks (classification, object detection, semantic segmentation, text summarization, question and answering, sentiment analysis) from the official ONNX model hub. We detected 15 issues, 14 of which were previously unknown, associated with optimizer crashes and accuracy deviations. We also observed $9.2$% of all model instances presenting issues leading into the crash of the optimizer, or the generation of an invalid model while using the primary optimizer strategies. In addition, $30$% of the classification models presented accuracy differences across the original and the optimized model variants, while $16.6$% of semantic segmentation and object detection models are also affected, at least to a limited extent.

Automated Sentiment Classification and Topic Discovery in Large-Scale Social Media Streams

We present a framework for large-scale sentiment and topic analysis of Twitter discourse. Our pipeline begins with targeted data collection using conflict-specific keywords, followed by automated sentiment labeling via multiple pre-trained models to improve annotation robustness. We examine the relationship between sentiment and contextual features such as timestamp, geolocation, and lexical content. To identify latent themes, we apply Latent Dirichlet Allocation (LDA) on partitioned subsets grouped by sentiment and metadata attributes. Finally, we develop an interactive visualization interface to support exploration of sentiment trends and topic distributions across time and regions. This work contributes a scalable methodology for social media analysis in dynamic geopolitical contexts.

Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs

Multimodal Sentiment Analysis (MSA) is a rapidly developing field that integrates multimodal information to recognize sentiments, and existing models have made significant progress in this area. The central challenge in MSA is multimodal fusion, which is predominantly addressed by Multimodal Transformers (MulTs). Although act as the paradigm, MulTs suffer from efficiency concerns. In this work, from the perspective of efficiency optimization, we propose and prove that MulTs are hierarchical modal-wise heterogeneous graphs (HMHGs), and we introduce the graph-structured representation pattern of MulTs. Based on this pattern, we propose an Interlaced Mask (IM) mechanism to design the Graph-Structured and Interlaced-Masked Multimodal Transformer (GsiT). It is formally equivalent to MulTs which achieves an efficient weight-sharing mechanism without information disorder through IM, enabling All-Modal-In-One fusion with only 1/3 of the parameters of pure MulTs. A Triton kernel called Decomposition is implemented to ensure avoiding additional computational overhead. Moreover, it achieves significantly higher performance than traditional MulTs. To further validate the effectiveness of GsiT itself and the HMHG concept, we integrate them into multiple state-of-the-art models and demonstrate notable performance improvements and parameter reduction on widely used MSA datasets.

Emotions in the Loop: A Survey of Affective Computing for Emotional Support

In a world where technology is increasingly embedded in our everyday experiences, systems that sense and respond to human emotions are elevating digital interaction. At the intersection of artificial intelligence and human-computer interaction, affective computing is emerging with innovative solutions where machines are humanized by enabling them to process and respond to user emotions. This survey paper explores recent research contributions in affective computing applications in the area of emotion recognition, sentiment analysis and personality assignment developed using approaches like large language models (LLMs), multimodal techniques, and personalized AI systems. We analyze the key contributions and innovative methodologies applied by the selected research papers by categorizing them into four domains: AI chatbot applications, multimodal input systems, mental health and therapy applications, and affective computing for safety applications. We then highlight the technological strengths as well as the research gaps and challenges related to these studies. Furthermore, the paper examines the datasets used in each study, highlighting how modality, scale, and diversity impact the development and performance of affective models. Finally, the survey outlines ethical considerations and proposes future directions to develop applications that are more safe, empathetic and practical.