Social Sentiment Insights (SSi)
FinSoftAI (FinSoft AI Solutions Private Limited) · Marketing · Business
Social Sentiment Insights (SSi) is FinSoftAI's narrative intelligence platform. It reads news, social posts and search data, then turns them into explainable market signals for traders and analysts.
About Social Sentiment Insights (SSi)
What Is Social Sentiment Insights (SSi)
Social Sentiment Insights (SSi) is an AI research platform for people who trade and study U.S. equities. FinSoftAI built it around one claim: narratives move prices before the price chart shows anything. SSi collects financial news, social media chatter, ESG commentary and search behavior, then scores how a story about a company is building or fading.
The pitch differs from a plain sentiment analysis platform that hands you a number. Most tools hand you a bullish or bearish number and stop there. SSi tries to show who is driving the story, which sources carry weight, and how long the shift has been running. Signals come with a confidence score and a source trail, so an analyst can check the reasoning instead of trusting a black box.
The limits matter too. Coverage centers on U.S. listed stocks, the product is sold to firms through a sales conversation, and pricing isn't published. There's no free tier for hobby use beyond a trial, and the platform assumes you already know how to read a market.
Getting Started
- Request a demo or trial through the FinSoftAI site so the team can set up credentials and API limits.
- Open ssi.finsoftai.com and sign in with Google. Skip the email and password fields, since SSi uses Google Sign-In.
- Accept the license agreement that appears after your first sign-in.
- Run the built-in tour and watch the three short video tutorials before touching live data.
- Configure your feeds, sentiment weights, technical indicators and alert thresholds, then save a query you can reuse.
Product Information
A quick look at Social Sentiment Insights (SSi)'s pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Sell-side research analysts
- Buy-side portfolio managers
- Risk and ESG officers
- Independent traders with a data budget
Tasks
- Spotting narrative and price divergence
- Screening a watchlist before the open
- Reviewing ESG perception
- Building an audit trail
Scenarios
- Earnings season
- Sector rotation
- A social risk scare
Key features
Narrative clustering instead of a sentiment number
SSi groups related posts, articles and searches into narrative clusters, then ranks them by market-moving strength. You see the story, the named entities behind it and the sources carrying it. That context is the difference between "sentiment is 62" and "sentiment is 62 because three credible outlets picked up a supply chain story this morning."
Configurable sentiment engines and weights
You aren't locked into one scoring model. SSi ships with its own engine and lets you plug in third-party engines, then weight each source feed to match how your desk actually thinks. A trader who trusts filings more than forums can tune the model that way and keep the same interface.
Multi-horizon sentiment charts against price and volume
The platform plots sentiment against price, and sentiment volume against trading volume, across 7, 14, 30 and 60 day horizons. FinSoftAI normalizes those readings into Z-scores, which makes it possible to rank more than 100 U.S. equities on one scale. For a human reader, the practical gain is seeing whether a narrative is accelerating while the stock stalls.
ESG sentiment scores by sector
E, S and G scores are built from public sentiment rather than company disclosure, and the weights shift by sector. It's market sentiment data with a narrower job than a general index. A bank gets judged on governance and conduct, an industrial on environmental exposure. That design keeps the score closer to how the sector is actually discussed.
SSi Research terminal
Research is the entry-level component: a standalone terminal with historical data, an NLP query builder, sentiment vs price charts and search analytics. It skips live alerting but covers the research workflow, which is why FinSoftAI positions it for both institutions and individual subscribers.
APIs and headless deployment
SSi runs with its own interface or headless. The social sentiment API suite covers ticker sentiment, sector aggregation, ESG scoring, sentiment OHLC data, sentiment volume and alerts, with low latency access aimed at quants. Firms that already have an order management or research stack can pull signals in instead of switching screens.
Pros and cons
Pros
- Signals arrive with a source trail and a confidence score, so an analyst can challenge a call instead of accepting it.
- The modular build lets a firm buy the Research terminal first and add ESG, risk or API components later.
- Sentiment weights, engines, feeds and technical indicators are all configurable, which fits desks with an existing process.
- ESG scores come weighted by sector, which is more useful than one blended number for a diversified book.
- FinSoftAI publishes its own live trading record, including losing trades and drawdown, rather than only backtests.
Cons
- Pricing isn't listed anywhere public. You need a sales call before you know whether the platform fits a small budget.
- Coverage is built around U.S. equities, so multi-market desks will find gaps outside that universe.
- The Research terminal is historical only, which means no live alerts until you move to a higher component.
- Getting real value takes setup work. Feed selection, weight tuning and query design all need someone who understands the desk's process.
Frequently asked questions
It converts unstructured text about companies into scored market signals. News, social posts, ESG commentary and search behavior get clustered into narratives, then measured for strength and direction so you can compare them with price action.
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