Are You Sure You're Positive? Consolidating Chain-of-Thought Agents with Uncertainty Quantification for Aspect-Category Sentiment Analysis

Aspect-category sentiment analysis provides granular insights by identifying specific themes within product reviews that are associated with particular opinions. Supervised learning approaches dominate the field. However, data is scarce and expensive to annotate for new domains. We argue that leveraging large language models in a zero-shot setting is beneficial where the time and resources required for dataset annotation are limited. Furthermore, annotation bias may lead to strong results using supervised methods but transfer poorly to new domains in contexts that lack annotations and demand reproducibility. In our work, we propose novel techniques that combine multiple chain-of-thought agents by leveraging large language models' token-level uncertainty scores. We experiment with the 3B and 70B+ parameter size variants of Llama and Qwen models, demonstrating how these approaches can fulfil practical needs and opening a discussion on how to gauge accuracy in label-scarce conditions.

Shifting NER into High Gear: The Auto-AdvER Approach

This paper presents a case study on the development of Auto-AdvER, a specialised named entity recognition schema and dataset for text in the car advertisement genre. Developed with industry needs in mind, Auto-AdvER is designed to enhance text mining analytics in this domain and contributes a linguistically unique NER dataset. We present a schema consisting of three labels: "Condition", "Historic" and "Sales Options". We outline the guiding principles for annotation, describe the methodology for schema development, and show the results of an annotation study demonstrating inter-annotator agreement of 92% F1-Score. Furthermore, we compare the performance by using encoder-only models: BERT, DeBERTaV3 and decoder-only open and closed source Large Language Models (LLMs): Llama, Qwen, GPT-4 and Gemini. Our results show that the class of LLMs outperforms the smaller encoder-only models. However, the LLMs are costly and far from perfect for this task. We present this work as a stepping stone toward more fine-grained analysis and discuss Auto-AdvER's potential impact on advertisement analytics and customer insights, including applications such as the analysis of market dynamics and data-driven predictive maintenance. Our schema, as well as our associated findings, are suitable for both private and public entities considering named entity recognition in the automotive domain, or other specialist domains.

Natural language processing on customer note data

Automatic analysis of customer data for businesses is an area that is of interest to companies. Business to business data is studied rarely in academia due to the sensitive nature of such information. Applying natural language processing can speed up the analysis of prohibitively large sets of data. This paper addresses this subject and applies sentiment analysis, topic modelling and keyword extraction to a B2B data set. We show that accurate sentiment can be extracted from the notes automatically and the notes can be sorted by relevance into different topics. We see that without clear separation topics can lack relevance to a business context.