Crypto Social
Intelligence.

Crypto sentiment analysis is the practice of measuring how bullish, bearish or emotionally charged the market feels toward a token by reading what people say about it. Ruma reads every relevant social post with large language models rather than counting keywords, then distils each one into structured sentiment signals — a sentiment score, a fear and greed reading, an emotional breakdown and more — for individual assets and their communities.

Ruma continuously ingests content from across X, Reddit, YouTube and crypto news, scores each item with proprietary machine-learning models, and rolls the results up into live indices per token. Because scoring happens as content arrives, the signals update in near real time, so you can see sentiment shift on a narrative, a listing or a market move as it happens rather than hours later.

A Fear and Greed Index condenses market emotion into a single number, where extreme fear can mark a bottom and extreme greed can mark a top. Most public indices, like the ones from Alternative.me and CoinMarketCap, track Bitcoin. Ruma extends the same idea to every asset it covers, giving you a per-token fear and greed reading so you can fade the crowd on any coin, not just BTC.

Yes. Ruma tracks individual accounts and key opinion leaders (KOLs) so you can follow what specific people are posting, how bullish or bearish they are, and which calls they got right. You can surface the accounts that actually move markets, see who called a top or a bottom, and read a single person’s posts and sentiment over time.

Ruma applies proprietary machine-learning and large language models to every post it ingests. Instead of simple keyword matching, the models interpret meaning, tone, sarcasm and conviction, then output structured signals such as sentiment score, emotion, and community conviction. This lets Ruma capture nuance that traditional mention-counting tools miss and turn unstructured social chatter into clean, machine-readable data.

Ruma pulls from every major place the crypto conversation happens — X, Reddit, YouTube and crypto news — bringing all of these sources together in one place. Every post, thread, video and headline is scored by Ruma’s own models and aggregated into per-token and per-community indices, so the signals reflect the full live conversation across platforms rather than a single feed.

Yes. Every post Ruma ingests is tagged by topic and event, so you can filter the feed down to exactly the conversation you care about — airdrops, exchange listings and delistings, hacks and exploits, scam warnings, fundraising rounds, ETF and IPO news, whale activity, technical analysis, rumours and breaking-news catalysts. You can combine these filters with a specific token, sentiment or time window, and every filter is exposed through the API so you can pull the same tightly-scoped feed into your own tools.

Yes. Ruma classifies the people behind each post, so you can filter by who is talking as well as what they are saying. Read only key opinion leaders (KOLs), zoom in on a single named account, or pull whole cohorts such as founders, CEOs and other executives, developers, exchanges or media. That lets you answer questions like “what are founders posting about this listing?” or “how are CEOs reacting to the hack?” — and, because the classification is available over the API, you can build the same audience filters into your own dashboards and trading systems.

Social Momentum is a proprietary Ruma metric that shows post volume over time broken down by the author clusters driving the conversation, drawn as a stream graph where each coloured band is a distinct group and its thickness is how loudly that crowd is posting. It matters because who is talking is often more revealing than how much is being said: a narrative that has only reached day traders and shillers looks very different from one that has spread into developers, VCs, on-chain analysts and long-term position traders. By showing how many social groups a token has hit and exactly which ones — from short-term day traders to long-horizon position traders, founders, funds and smart money — Social Momentum lets you see whether attention is broadening into higher-quality cohorts or fading back into the same small crowd.

Ruma classifies every account behind a post into one of nearly thirty author clusters, grouped into families. Builders: developers, founders, team leaders and project accounts. Capital and institutions: VCs, asset managers, DAO and guild funds, incubators and exchanges. Influence and media: KOLs, celebrities, media, marketers and shillers. Research and analysis: technical (TA) analysts, fundamental (FA) analysts, macro analysts, on-chain analysts and investigators. Traders, split by time horizon: position traders, swing traders and day traders, plus tribalists. Predispositions: perma bulls and doomers. Other entities: policy and political accounts, NFT communities, bots and automated or AI accounts. On top of these author clusters, Ruma also tracks two signal groups derived from track record — smart money (the top callers with the strongest history of early, correct calls) and top degens (the most active high-conviction speculators).

Yes, and this is something no other tool offers. Ruma computes sentiment, mindshare and post volume not just for a token overall but for every individual author cluster and any combination of them. That means you can isolate exactly one crowd — how bullish are the VCs, what is smart money’s mindshare, how much are developers actually posting — or blend several clusters into a custom cohort and read the same metrics for that group. Because the breakdown is per cluster and per combination, you can answer questions ordinary mention-counting or single-number sentiment tools simply cannot.

Yes. Ruma lets you filter any signal down to a single cluster, so you can pull the sentiment of just VCs, just smart money, just founders, just day traders, or any other tracked group, and watch it over time. This is useful for spotting divergence — for example when smart money and VCs turn bullish while retail day traders are still bearish, or when founders and developers stay quiet during a hype-driven rally. Every cluster-level sentiment, mindshare and volume reading is also available through the API, so you can wire a specific cohort’s positioning straight into your own dashboards or trading models.

Yes. Ruma exposes its sentiment signals, indices and social metrics through an API so you can pull the same data that powers the app directly into your own stack. That makes it straightforward to build sentiment-driven strategies, backtest against historical signals, and feed real-time readings into an algorithmic or quantitative trading system.

Ruma is built to be consumed by machines as well as people. Because every signal is structured and available over the API, autonomous and agentic trading systems can query live sentiment, fear and greed, and smart-money positioning as inputs to their decisions. Ruma also offers an in-app intelligence agent for exploring the data conversationally.

The Cultiness Index is a proprietary Ruma metric that measures how cult-like a token’s community really is. It reads the near-religious, high-conviction belief in the language people post — the kind of conviction that holds through every drawdown and stays decoupled from price. It is a signal you won’t find in traditional sentiment tools and can help identify communities with unusually resilient holders.

Ruma surfaces smart-money calls by tracking the bullish and bearish positioning of the accounts that historically move markets. You can see who called the top or the bottom on a given asset and drill into any name to understand the conviction behind their calls, giving you a read on where informed participants are leaning.

Ruma goes beyond a simple green-or-red score to surface the full spectrum of emotions moving a market. It tracks fifteen distinct emotions, organised as opposite pairs: joy versus sadness, optimism versus pessimism, trust versus distrust, confidence versus fear, anticipation versus boredom, love versus disgust, and greed versus anger — plus surprise. This emotional breakdown helps you distinguish, for example, between a rally driven by genuine conviction and confidence and one driven by short-lived greed or euphoria.

Most sentiment tools count mentions or apply basic positive-versus-negative tagging. Ruma reads every post with large language models and produces proprietary indices you won’t find elsewhere: a fear and greed reading for any token, a cultiness score for its community, a full emotional breakdown, and smart-money positioning. The result is deeper, more actionable signal rather than raw volume.

Ruma provides its metrics across all assets, with over 100,000 projects already indexed in our database and new projects becoming supported automatically. Signals like fear and greed and the Cultiness Index extend across every one of them rather than just the majors. Coverage is driven by where the conversation is happening — if there is chatter about a token, Ruma will have it — so emerging narratives and communities are captured alongside established assets.

Mindshare is the share of the overall crypto conversation that a single token commands at any given moment. Ruma measures it by tracking how much of the social discussion is dedicated to each asset relative to the rest of the market, so you can watch attention rotate between narratives and spot a token gaining or losing relevance before it shows up in price.

Social volume measures how much people are talking about a token, while sentiment measures how they feel about it. Ruma tracks post volume over time alongside sentiment, so you can tell the difference between quiet conviction and a loud, crowded trade — a spike in volume with softening sentiment often signals froth, while rising sentiment on steady volume can mark genuine accumulation.

Ruma retains historical sentiment, fear and greed, mindshare and other signals so you can chart how a token’s social picture has evolved rather than seeing only a live snapshot. That history lets you compare current readings against past regimes and backtest sentiment-driven strategies against how the signals actually moved.

Extreme readings are often the most useful. Historically, extreme greed and euphoric social sentiment can coincide with local tops, while extreme fear and capitulation can mark bottoms. Ruma’s fear and greed and emotion signals are built to make these extremes visible per token, so you can fade an overheated crowd or look for exhaustion instead of reacting to price alone.

Yes. Ruma tracks the follower count of a project’s accounts over time, so you can see whether a token’s audience is genuinely expanding or stagnating. Follower growth sits alongside sentiment and mindshare as part of the full social picture, helping you separate durable community building from short-lived hype.

Yes. The per-token fear and greed reading, like Ruma’s other signals, is available through the API, so you can display a live fear and greed gauge in your own dashboard, bot or app for any covered asset rather than being limited to a single Bitcoin-only index.

You can explore Ruma’s live intelligence in the app or pull the same data into your own stack through the API. Full endpoint reference and integration details are available in the Ruma API documentation, so developers and quantitative teams can wire sentiment signals directly into dashboards, models and trading systems.