Refined and Segmented Price Sentiment Indices from Survey Comments

Paper: arXiv 2411.09937

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper employs practical, data-driven NLP techniques (LLMs) and focuses on building indices with high empirical validation (correlation metrics against established CPI/PPI), but lacks advanced mathematical derivations or statistical modeling.

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance price sentiment indices<br>using Economy Watchers Survey comments"] --> B
  
  subgraph B ["Data & Preprocessing"]
      direction LR
      B1["Survey Comments<br>with Industry Metadata"] --> B2["Extract Price-Related Comments"]
  end

  B --> C{"LLM Classification"}
  C --> D["Classify Perspective:<br>Consumer vs. Business"]
  D --> E["Classify Sector:<br>Goods vs. Services"]

  subgraph F ["Sentiment Aggregation"]
      direction LR
      F1["Construct Indices:<br>General, Consumer, Business"] --> F2["Industry-Selection<br>for Consumer Samples"]
  end

  E --> F

  F --> G["Key Findings & Outcomes"]
  G --> H["Higher correlation to<br>existing price indices"]
  G --> I["Potential performance gain<br>via multiple LLM integration"]

Cited References (41)

1. Economy Watchers Survey provides Datasets and Tasks for Japanese Financial Domain (2024) — link
2. Extracting firms’ short-term inflation expectations from the Economy Watchers Survey using text analysis (2021)
3. S udachi: a J apanese Tokenizer for Business (2018) — link
4. Constructing and analyzing domain-specific language model for financial text mining (2023) — link
5. FinDeBERTaV2: Word-Segmentation-Free Pre-trained Language Model for Finance (2024) — link
6. The causal relationship between producer price index and consumer price index: Empirical evidence from selected European countries (2011)
7. Dynamic causality between PPI and CPI in China: A rolling window bootstrap approach (2023)
8. Exchange rate pass-through to Japanese prices: Import prices, producer prices, and the core CPI (2022) — link

2 of 41 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.