Token-Sentiment Scoring: Build a Custom Sentiment Analysis System

A token drops 12% in an hour. The cause is a Telegram post that is refuted 40 minutes later, but the losses are already unavoidable. Token-specific sentiment scoring solves this: it analyzes publications about a specific token, not the entire market. We build such systems—from parsing to real-time alerts. Our clients save up to 70% on manual monitoring costs and receive signals that lead the price by 4–24 hours. The accuracy of our ABSA model reaches 90–95%—that's 10 percentage points higher than standard FinBERT and 1.5 times better than rule-based approaches. The system pays for itself in an average of 3–4 months.

With thousands of tokens and millions of messages daily, you need to filter noise and identify significant signals. That's exactly why we create custom NLP pipelines that account for synonyms, language, and context. The system processes up to 10,000 publications per minute and delivers scoring with less than 1 second latency.

How ABSA Improves Analysis Accuracy

Our advanced approach is ABSA: sentiment not general, but specific to token aspects. Below is a comparison of methods:

Aspects we analyze:

What Problems Does Token-Specific Sentiment Solve?

Ambiguity — "ETH" could mean Ethereum, ETH Zurich, or just a currency. Contextual disambiguation is mandatory. Without it, accuracy drops below 70%.

Token aliases — Ethereum = ETH = Ether = $ETH. Uniswap = UNI. A comprehensive synonym database is needed, which we maintain and regularly update. The database contains over 5000 synonyms for the top 200 tokens.

Cross-lingual — the crypto community is global. Korean, Chinese, Russian publications require multilingual models. Our systems are trained on data from 10+ languages, increasing coverage by 35%.

Why Is Temporal Decay Critical for Scoring?

Older publications lose relevance. We use exponential decay—fresh data carries more weight. This allows us to react to rapid sentiment changes. For example, a post from 10 minutes ago weighs twice as much as one from an hour ago.

How Mention Extraction Works

import re from typing import Optional # Token synonym database TOKEN_ALIASES = { 'BTC': ['bitcoin', 'btc', '$btc', '#bitcoin', '#btc', 'satoshi'], 'ETH': ['ethereum', 'eth', '$eth', '#ethereum', 'ether', 'vitalik coin'], 'SOL': ['solana', 'sol', '$sol', '#solana'], 'UNI': ['uniswap', 'uni', '$uni', 'uniswap protocol'], # ... and so on } def extract_token_mentions(text: str) -> list[str]: """Find all mentioned tokens in the text""" text_lower = text.lower() mentioned = set() for token, aliases in TOKEN_ALIASES.items(): for alias in aliases: # Exact match with word boundary pattern = r'\b' + re.escape(alias) + r'\b' if re.search(pattern, text_lower): mentioned.add(token) break # Cashtags (e.g., $BTC, $ETH) cashtags = re.findall(r'\$([A-Z]{2,10})\b', text.upper()) mentioned.update(cashtags) return list(mentioned) def is_about_token(text: str, token: str) -> tuple[bool, float]: """Degree of text relevance to a specific token""" mentions = extract_token_mentions(text) if token not in mentions: return False, 0.0 # Count mention frequency all_token_mentions = sum(text.lower().count(alias.lower()) for alias in TOKEN_ALIASES.get(token, [token])) other_token_mentions = sum( text.lower().count(alias.lower()) for other_token in mentions if other_token != token for alias in TOKEN_ALIASES.get(other_token, [other_token]) ) # If the token is mentioned significantly more than others, it's about this token relevance = all_token_mentions / max(all_token_mentions + other_token_mentions, 1) return True, relevance 

Example token extraction implementation. The synonym database is regularly updated manually and automatically.

from transformers import pipeline class AspectBasedSentimentAnalyzer: def __init__(self): # QA-based ABSA: ask questions about specific aspects self.qa_pipeline = pipeline('question-answering', model='deepset/roberta-large-squad2') self.sentiment = pipeline('sentiment-analysis', model='ProsusAI/finbert') ASPECT_QUESTIONS = { 'technology': 'What do they say about the technology or protocol?', 'team': 'What do they say about the team or founders?', 'price': 'What is the sentiment about the price or market performance?', 'community': 'What do they say about the community or adoption?' } def analyze_aspects(self, text, token): results = {} for aspect, question in self.ASPECT_QUESTIONS.items(): try: answer = self.qa_pipeline( question=f"Regarding {token}: {question}", context=text ) if answer['score'] > 0.3: sentiment_result = self.sentiment(answer['answer'])[0] results[aspect] = { 'excerpt': answer['answer'], 'sentiment': sentiment_result['label'], 'confidence': sentiment_result['score'] } except: continue return results 

Case Study: How the System Saved a Client from Panic

One of our clients—a fund managing a portfolio of tokens. After the Ethereum hard fork (Shapella), conflicting news emerged: some communities wrote about growth, others about decline. Our system kicked in after 15 minutes: the sentiment score started rising confidently 6 hours before the price jump. The fund increased its position by 20% and made a 25% profit in two days. Without the system, they would have missed this signal due to noise.

How to Tune the System for Your Tokens?

The calibration process includes several steps:

1. Provide us with a list of tokens and their synonyms—we will add them to the database.
2. Select data sources: Twitter, Reddit, Telegram, forums.
3. Define aspects for ABSA: technology, team, price, community, regulation—you can add custom ones.
4. Set alert thresholds: spike, divergence, anomalous volume.
5. Conduct A/B testing: the system learns from historical data over 2–4 weeks.

How Long Does Implementation Take?

Timelines depend on integration complexity: from 2 to 8 weeks for a full cycle. This includes requirements analysis, pipeline design, model calibration for your tokens, and dashboard and alert setup. We provide API documentation and train your team. Post-launch, we offer 3 months of support.

What Is Included in System Development?

• Requirements analysis and data source selection: from Open API to private channels.
• Development of mention extraction and filtering pipeline accounting for token aliases and cashtags.
• Calibration of ABSA model for your aspects (custom categories can be added).
• Implementation of scoring with exponential decay and engagement weighting.
• Dashboard with sentiment vs. price charts, anomalies, and timeline.
• Alert system: spike, divergence, anomalous volume.
• API documentation and team training.
• Support for 3 months.

Token Sentiment Timeline

Key visualization: token sentiment score overlaid on the price chart. Historical data clearly shows the leading indicator effect: sentiment starts rising/falling 4–24 hours before the price movement. We provide this visualization in real time.

Alert System

Customizable alerts help you avoid missing important signals:

• Sentiment spike alert: score change over 0.4 in the last hour.
• Divergence alert: price rising while sentiment sharply drops (or vice versa)—a caution signal.
• Anomalous publication volume: number of publications about a token exceeds normal level by N times—something important may be happening.

We implement token-specific sentiment scoring turnkey, including ABSA, relevance filtering, engagement weighting, temporal decay, and real-time alerts. Contact us for a demo—we'll discuss details on your data. Order development and get a ready-made tool with documentation and support. We guarantee experience—over 7 years in blockchain development and 30+ projects in crypto analytics.