Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings
원문 초록
Customers' reviews and comments are important for businesses to understand users' sentiment about the products and services. However, this data needs to be analyzed to assess the sentiment associated with topics/aspects to provide efficient customer assistance. LDA and LSA fail to capture the semantic relationship and are not specific to any domain. In this study, we evaluate BERTopic, a novel method that generates topics using sentence embeddings on Consumer Financial Protection Bureau (CFPB) data. Our work shows that BERTopic is flexible and yet provides meaningful and diverse topics compared to LDA and LSA. Furthermore, domain-specific pre-trained embeddings (FinBERT) yield even better topics. We evaluated the topics on coherence score (c_v) and UMass.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
LLMs Are Not Scorers: Rethinking MT Evaluation with Generation-Based Methods
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Sentence Embeddings as an intermediate target in end-to-end summarisation
JTCSE: Joint Tensor-Modulus Constraints and Cross-Attention for Unsupervised Contrastive Learning of Sentence Embeddings
Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks
Domain Adaptation for Japanese Sentence Embeddings with Contrastive Learning based on Synthetic Sentence Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.