AI Social Listening: Mention & Trend Monitoring

AI Social Listening: Mention & Trend Monitoring

Picture this: your brand is being discussed in thousands of posts, but you find out a week later when the reputation is already damaged. Or you spend hours on manual monitoring, missing important trends. An AI social listening system solves both problems in real time. We develop such systems turnkey: from source integration to an insight dashboard. Our experience in NLP and MLOps—over 50 projects in over 7 years—allows us to achieve analysis accuracy of up to 95%.

Social listening is the monitoring of brand, product, person, and topic mentions across social networks, forums, and media, followed by sentiment, reach, and trend analysis. The difference from basic monitoring is understanding context and insights, not just a mention counter.

The key problem is noise. Without quality filtering, 80% of data is useless. We use multi-layer filtering: first regex and key-phrase matchers, then semantic matching via embeddings (1536-dim). This reduces false positives by 5x compared to rule-based approaches.

Sources and Parsing

Below are the main sources and their characteristics:

For each source, we configure individual rate limits and duplicate filtering.

Real-Time Monitoring System

class SocialListeningSystem: def __init__(self, brand_profile: BrandProfile): self.brand_profile = brand_profile self.sentiment_model = load_sentiment_model("ru") self.mention_extractor = MentionExtractor(brand_profile) async def process_stream(self, post: SocialPost) -> MentionEvent | None: # Relevance check: brand mention or keywords if not self.mention_extractor.is_relevant(post.text): return None # Aspect-based sentiment analysis sentiment = self.sentiment_model.analyze(post.text) aspects = self.extract_aspects(post.text, self.brand_profile.aspect_list) # Reach and virality estimation reach = estimate_reach(post) mention = MentionEvent( source=post.source, url=post.url, text=post.text, author=post.author, published_at=post.published_at, sentiment=sentiment.label, sentiment_score=sentiment.score, aspects=aspects, reach=reach, priority=self.calculate_priority(sentiment, reach), requires_response=self.needs_response(sentiment, post) ) # High-priority mentions trigger immediate alert if mention.priority == "P1": await self.alert_team(mention) return mention def needs_response(self, sentiment, post) -> bool: # Negative reviews with a question or complaint require response return (sentiment.label == "negative" and (post.has_question or post.is_complaint) and post.author_followers > 100) 

Why Aspect-Based Sentiment Analysis Is More Effective

A general "negative" sentiment gives little information. Aspect-based analysis reveals exactly what is being criticized: for e-commerce, that's delivery, product quality, support, pricing; for a bank, it's the mobile app, credit terms, branches, products. We use chain-of-thought prompts to boost accuracy. In an A/B test on 10,000 reviews, our approach achieved F1=0.89 versus 0.72 for trivial zero-shot.

class AspectMention(BaseModel): aspect: str sentiment: str # positive / negative / neutral quoted_text: str # quote from original def extract_aspects(text: str, aspect_list: list[str]) -> list[AspectMention]: prompt = f"""Проанализируй тональность текста по каждому аспекту. Аспекты: {', '.join(aspect_list)} Текст: {text} Для каждого упомянутого аспекта укажи тональность и цитату.""" return llm.parse(prompt, response_format=list[AspectMention]) 

How Are Trends Detected in Real Time?

Trends are anomalous growth in mention count or a sentiment shift. Alerts trigger when:

• Mention growth > 3σ in the last 2 hours (crisis pattern)
• Sharp sentiment shift to negative
• Viral post mentioning the brand (reach > 100k)

Algorithm: moving average + Z-score for anomaly detection, ADTK for time series.

Competitive Comparison

Parallel monitoring of competitors provides relative sentiment—not just "we are criticized" but "we are criticized less/more than competitors." Share of Voice: the share of brand mentions in the total category mentions.

AI sentiment analysis is 3x more accurate than non-specialized solutions: our model achieves F1=0.88 versus 0.65 for open-source libraries without fine-tuning.

What's Included in Development?

1. Source audit: list of relevant platforms, setup of API keys and parsers.
2. Architecture design: stack selection (PyTorch/HuggingFace, LangChain, ChromaDB/Weaviate), data flow design.
3. Model development: fine-tuning or few-shot adaptation of LLM to client's aspects, INT4 quantization to reduce p99 latency.
4. Dashboard integration: real-time metrics in Power BI/Grafana, scheduled auto-reports.
5. Documentation and training: API spec, operation manual, training for two employees.
6. Warranty support: 3 months free support, extendable on request.

Our Experience and Guarantees

We have delivered over 50 social listening projects for banks, retailers, and telecom companies. Pilot launch in as little as 2 weeks. We guarantee model accuracy (minimum F1=0.85) and system response time SLA. Get a consultation for your project—we'll assess the sources and prepare an architecture.

Process of Work

Analysis → Design → Development → Testing → Deployment. At each stage, we demo to the client for approval. Timelines: 4 to 12 weeks. Pricing is per-project, turnkey. Contact us to discuss the details.