opinion-analyst

name	opinion_analyst
display_name	Opinion Analyst
description	Public opinion analysis and sentiment at scale — sentiment scoring, stance detection, and multi-dimensional bias measurement.
version	0.1.0
source_repo	666ghj/BettaFish
license	MIT
tier	experimental
capabilities	["sentiment","opinion_analysis","stance_detection","bias_measurement"]
allowed_tools	["analyze_sentiment","detect_stance","measure_bias"]
output_formats	["python_api","cli","mcp_server","agent_skill","rest_api"]

Opinion Analyst

Overview

OpinionAnalyst wraps 666ghj/BettaFish, a public-opinion analysis library built for high-throughput sentiment mining. The specialist surfaces three core capabilities — sentiment scoring, stance detection, and bias measurement — as callable tools within the OSS Agent Lab pipeline.

Capabilities

• sentiment: Document-, sentence-, and aspect-level sentiment scoring with a continuous score in [-1.0, 1.0].
• opinion_analysis: Holistic opinion mining that combines sentiment with aspect extraction for nuanced understanding.
• stance_detection: Determines whether the author supports, opposes, or is neutral toward a named entity or claim.
• bias_measurement: Measures political, emotional, framing, source-attribution, and confirmation bias; returns per-dimension scores and human-readable flags.

Tools

Tool	Description	Side Effects
analyze_sentiment	Score sentiment (positive/negative/neutral) with confidence and aspects	None
detect_stance	Classify stance (support/oppose/neutral) toward a target	None
measure_bias	Score bias across configurable dimensions, return flags	None

Usage

Python API

import asyncio
from agents.specialists.opinion_analyst.agent import OpinionAnalystSpecialist
from oss_agent_lab.contracts import Intent, Query, SpecialistRequest

specialist = OpinionAnalystSpecialist()

request = SpecialistRequest(
    intent=Intent(action="sentiment", domain="opinion", confidence=0.9),
    query=Query(user_input="The new policy is an outstanding step forward."),
    specialist_name="opinion_analyst",
)

result = asyncio.run(specialist.execute(request))
print(result.result)
# {'sentiment': {'sentiment': 'positive', 'confidence': 0.65,