# Refined and Segmented Price Sentiment Indices from Survey Comments

arXiv: https://arxiv.org/abs/2411.09937 · HTML: https://mlsanity.com/paper/2411.09937/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [FinBen: A Holistic Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2402.12659/) (2024)
- [Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities](https://mlsanity.com/paper/2404.17790/) (2024)
- [Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks](https://mlsanity.com/paper/2305.05862/) (2023)
- [Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models](https://mlsanity.com/paper/2402.03659/) (2024)
- [LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction](https://mlsanity.com/paper/2406.10811/) (2024)
- [LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs](https://mlsanity.com/paper/2407.03963/) (2024)
- [Construction of a Japanese Financial Benchmark for Large Language Models](https://mlsanity.com/paper/2403.15062/) (2024)
- [Large Language Models are legal but they are not: Making the case for a powerful LegalLLM](https://mlsanity.com/paper/2311.08890/) (2023)
- [Is ChatGPT a Financial Expert? Evaluating Language Models on Financial Natural Language Processing](https://mlsanity.com/paper/2310.12664/) (2023)
- [ChatGPT-based Investment Portfolio Selection](https://mlsanity.com/paper/2308.06260/) (2023)
- [From Base to Conversational: Japanese Instruction Dataset and Tuning Large Language Models](https://mlsanity.com/paper/2309.03412/) (2023)
- [News-based Business Sentiment and its Properties as an Economic Index](https://mlsanity.com/paper/2110.10340/) (2021)
- [JaFIn: Japanese Financial Instruction Dataset](https://mlsanity.com/paper/2404.09260/) (2024)
- [Economy Watchers Survey Provides Datasets and Tasks for Japanese Financial Domain](https://mlsanity.com/paper/2407.14727/) (2024)

## Built on by (most cited first, 1 of 1)

- [Wage Sentiment Indices Derived from Survey Comments via Large Language Models](https://mlsanity.com/paper/2509.00290/) (2025)

## Abstract (arXiv)

We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (LLM). We classify whether the survey sample reflects the perspective of consumers or businesses, and whether the comments pertain to goods or services by utilizing information on the fields of comments and the industries of respondents included in the Economy Watchers Survey. From these classified price-related comments, we construct price sentiment indices not only for a general purpose but also for more specific objectives by combining perspectives on consumers and prices, as well as goods and services. It becomes possible to achieve a more accurate classification of price directions by employing a LLM for classification. Furthermore, integrating the outputs of multiple LLMs suggests the potential for the better performance of the classification. The use of more accurately classified comments allows for the construction of an index with a higher correlation to existing indices than previous studies. We demonstrate that the correlation of the price index for consumers, which has a larger sample size, is further enhanced by selecting comments for aggregation based on the industry of the survey respondents.