Building an Emotion Signal System: From Market News and Comments to Smart Sentiment Scoring

When markets experience turbulence or unexpected events—whether bullish or bearish—traditional financial metrics and technical indicators often fail to capture the immediate emotional response of investors. However, it is precisely this swift shift in sentiment that provides some of the most valuable “unstructured signals” in investment decision-making.

This article explores how to leverage the Luckdata-wrapped Yahu Financials API to build an emotion signal system that integrates news, community discussions, and smart sentiment scoring. By blending structured and unstructured data, we can develop a multi-dimensional system for generating investment insights with sharper market responsiveness.

1. Importance of Sentiment Data and System Architecture

Modern investment research is increasingly data-driven and multi-modal. Sentiment data adds substantial value across several dimensions:

• Capturing event-driven market moves in real-time;

• Detecting shifts in market consensus and investor psychology;

• Supplementing lagging indicators like earnings reports and valuations;

• Supporting alpha generation and dynamic risk control mechanisms.

With the Luckdata API ecosystem, the following modules are central to building a sentiment analysis framework:

2. Extracting News Sentiment: Real-Time Event Detection

Market-moving events are often first reflected in the news. Luckdata provides structured access to news content, allowing developers to extract key information such as headlines, publication times, and initial sentiment labels (positive/neutral/negative).

Here’s an example of how to call the API:

import requests
def fetch_news(uuid):
url = "https://luckdata.io/yahu-financials/4t4jbotgu79n"
params = {"uuid": uuid, "region": "US"}
headers = {"x-api-key": "YOUR_API_KEY"}
res = requests.get(url, headers=headers, params=params)
return res.json()

By applying keyword rules or NLP, sentiment classification can be further refined:

• Keywords like "beat expectations", "strong quarter" typically indicate positive sentiment.

• Phrases such as "downgrade", "miss revenue" are commonly tied to negative sentiment.

Aggregating such tagged news items daily by stock symbol allows for the creation of a news sentiment index, providing an early-warning indicator of directional market moves.

3. Monitoring Community Discussion Heat: Gauging Investor Buzz

Social media discussions have become a powerful barometer for market sentiment. The Luckdata Conversations module is akin to a U.S. stock-focused version of platforms like Xueqiu, Weibo, or Reddit. It enables real-time tracking of investor commentary and crowd psychology.

Here's how to retrieve the latest comments for a stock:

def fetch_comments(messageBoardId):
url = "https://luckdata.io/yahu-financials/wjbchky2ls76"
params = {"count": 16, "offset": 0, "sort_by": "newest", "messageBoardId": messageBoardId}
headers = {"x-api-key": "YOUR_API_KEY"}
res = requests.get(url, headers=headers, params=params)
return res.json()

Furthermore, by using /conversations/count, it’s possible to create a comment volume tracker that detects spikes in user activity. For instance, comparing today’s comment count to the 7-day average helps identify abnormal activity:

def detect_heat_spike(today_count, past_avg):
if today_count > 2 * past_avg:
return "Community heat spike detected!"

This approach can reveal rising retail interest in a stock and signal potential momentum-driven movements.

4. Smart Sentiment Scoring: Machine-Learned Signal Aggregation

For those looking to avoid manual labeling and modeling, Luckdata’s get-insights API offers a comprehensive smart scoring engine. It combines sentiment, valuation, momentum, and institutional focus into a unified view of a stock’s outlook.

Example usage:

def get_stock_insights(symbol="AAPL"):
url = "https://luckdata.io/yahu-financials/gev7puyjuroz"
params = {"symbol": symbol}
headers = {"x-api-key": "YOUR_API_KEY"}
res = requests.get(url, headers=headers, params=params)
return res.json()

Key output fields include:

• "bullishPercent": Indicates the market’s bullish sentiment;

• "sectorRelativePerformance": Benchmarks performance within its sector;

• "valuationScore" and "technicalScore": Scores for valuation and technical momentum.

These scores can be incorporated into multi-factor models or used as standalone sentiment signals in screening or alert systems.

5. Applying Sentiment Factors in Live Trading

Integrating sentiment signals into real-world investment systems unlocks numerous strategic opportunities:

1. Market timing strategies: Use sentiment reversals to inform entry or exit timing;

2. Enhanced stock scoring systems: Blend sentiment metrics into ranking models;

3. Event-driven backtesting frameworks: Study whether sentiment-aligned news predicts alpha;

4. Sentiment-valuation divergence detection: Identify overpriced stocks losing emotional support;

5. Unstructured signals in multi-factor models: Boost robustness and adaptability of factor investing frameworks.

An example of a composite sentiment score:

composite_score = (0.4 * news_sentiment_score
+ 0.3 * comments_volume_change
+ 0.3 * insights['bullishPercent'])

Such a score can drive stock ranking, portfolio rebalancing, or anomaly detection modules.

6. The Value of Luckdata Sentiment APIs

Compared to manually scraping websites or parsing raw unstructured news, Luckdata’s wrapped APIs offer several advantages:

• Highly structured data for news, comments, and sentiment scores;

• Easy event targeting via UUID or symbol-level access;

• Built-in smart scoring and analyst consensus indicators;

• Ideal for sentiment backtesting, scoring engines, and anomaly detection workflows;

• No need to manage scraping logic or anti-crawling systems—stable and developer-friendly.

By leveraging Luckdata’s sentiment APIs, investors and developers can construct more responsive, insightful, and explainable investment frameworks—perfect for use cases ranging from quant research to intelligent asset management.

Articles related to APIs :

• Quantitative Trading Strategy Development and Backtesting Based on Yahu API

• Building an Intelligent Stock Screening and Risk Alert System: A Full-Process Investment Research Workflow Using Luckdata Yahu API

• Build Your Financial Data System from Scratch: Bring the Financial World into Your Local Project via API

• Decoding Deep Stock Insights: Build Your Stock Analysis Radar with the Yahu API

• Financial Forums Aren’t Just Noise: Using the Yahu API to Decode Market Sentiment

• The Next Evolution in Portfolio Management: Building Your "Asset Pool + NAV Dashboard" with the Watchlists Module