Vamsi Krishna Pasam

In today's digital landscape, collaborative tools are critical for virtual teamwork, with comments as a key mechanism for communication and feedback. Our project, within the Natural Language Processing (NLP) domain, focuses on improving comment handling in collaborative environments using advanced machine learning methods. We developed a triage system that categorizes and prioritizes comments to help us efficiently address the most critical feedback. Building on previous work, we employed transformer models like BERT and RoBERTa, which showed strong performance in classifying comments when fine-tuned on our dataset. To enhance the handling of hierarchical structures, we experimented with Hierarchical Capsule Networks (HcapsNet) and Hierarchical Attention Networks (HAN). Additionally, GEMMA-2B, a large language model, demonstrated strong results in F1-score and precision while providing zero-shot and few-shot learning capabilities. The framework, tested in domains such as project management, academic collaboration, and document review, classifies and prioritizes comments based on six dimensions: urgency, importance, sentiment, actionability, resolution status, and thematic relevance.It incorporates rule-based logic alongside pre-trained NLP models, including GEMMA-2B for intent classification, Hugging Face models for sentiment analysis, and Latent Dirichlet Allocation (LDA) for topic modeling. This approach supports the efficient management of comments by prioritizing those that require immediate attention and improving the collaborative process.

The expansion of e-commerce requires accurate alignment of diverse multimodal content-such as user reviews, product images, and videos-with customer expectations. This study introduces a framework for mapping customer feedback to stages of the customer journey: Awareness, Consideration, Decision, and Post-Purchase. By analyzing user-generated content using Gemini 1.5 Flash, key features like sentiment, emotional tone, and product attributes were extracted to build a unified dataset. A hybrid classification approach was adopted, leveraging machine learning and rule-based logic. Among the models evaluated, Meta-LLaMA 3.2 3B and Meta-LLaMA 3.2-1B, large language models with robust contextual understanding, demonstrated the highest accuracy at 0.929, surpassing other models such as GPT-Neo, BERT, and RoBERTa. These results show how well the model captures complex customer opinion and aligns multimodal insights. The findings highlight discrepancies between consumer expectations and marketing narratives, particularly about the deliberation stage-a crucial phase in building trust. This research offers scalable and interpretable approaches to enhance customer journey mapping and improve content alignment in e-commerce.