Results for “sentiment”

Deep emotional sentiment analysis beyond positive/negative classification

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Multi-platform review analysis — pulls Yelp/Google/TripAdvisor reviews, identifies recurring complaints, sentiment patterns, response rate, and rating trajectory

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Analyze CSAT, NPS comments, reviews, and support feedback for sentiment, recurring themes, customer pain, and service improvement actions. Use for customer support and CX feedback reviews.

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Analyze customer message text for sentiment, urgency, and emotional tone.

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Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.

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Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text. Triggers: "text analytics", "sentiment analysis", "entity recognition", "key phrase", "PII detection", "TextAnalyticsClien...

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Use when the user asks to "track our share of voice", "what share of the conversation do we own vs competitors", or "trend our SOV this quarter"; computes SOV% = brand mentions ÷ (brand + competitor panel mentions) per platform per period on a LOCKED competitor panel — a panel switch invalidates the trend (restart the series and log the break; the ECHO O3 rule) — plus a sentiment-weighted SOV variant (sentiment labeled Estimated unless human-coded) and a Wikipedia-pageviews attention-share alternative; built from keyless listening connectors, gdelt.py news echo, and user exports — public counts only, closed platforms are never scraped. Not for backlink or offsite SEO signals — use offsite-signal-analyzer. 声量份额/竞品声量对比/提及份额/注意力份额

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Analyze meeting transcripts, recordings, and notes to extract action items, key decisions, topics, sentiment, and generate summaries using NLP and AI. Use when processing meeting transcripts, extracting action items, summarizing discussions, identifying key decisions, tracking topics and themes, analyzing sentiment, generating meeting notes, or building collaboration and productivity tools.

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Detect crisis signals in user content using NLP, mental health sentiment analysis, and safe intervention protocols. Implements suicide ideation detection, automated escalation, and crisis resource integration. Use for mental health apps, recovery platforms, support communities. Activate on "crisis detection", "suicide prevention", "mental health NLP", "intervention protocol". NOT for general sentiment analysis, medical diagnosis, or replacing professional help.

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Classify reply sentiment and determine next action

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Research community and social discussions from the last 30 days. Create a reusable Markdown briefing covering recent changes, praise, criticism, and recurring signals, with citations and clear limits on which sources were checked.

• Scanning trends discussed over the last 30 days
• Reviewing community reactions to a product launch
• Organizing recent praise and criticism

Train, deploy, and manage neural networks with Flow Nexus in isolated E2B environments. Use custom architectures or ready-made templates, run predictions, monitor distributed training across multiple environments, and evaluate models before sharing them.

• Train an image classification model
• Forecast time series with LSTM
• Deploy a sentiment analysis template

Turn interviews, surveys, support conversations, and public reviews into evidence-based customer insights. Identify recurring problems, buying triggers, and customer language, then build research reports, quote collections, or personas and plan interviews or surveys to fill gaps.

• Analyze customer interview transcripts
• Find recurring themes in reviews and support tickets
• Build customer personas from research evidence

Find ranked crypto tokens and trader addresses by social buzz, search popularity, capital inflows, or trading profit and loss. Compare results across supported blockchains, with time windows and filters available for relevant rankings.

• Find tokens attracting social buzz
• Compare tokens by net capital inflows
• Filter trending tokens by market size

Turn news, company announcements, and policy events into scored trading inputs. Store assessments in a standard CSV file, reduce their influence over time, and combine them with signals from price indicators to support event-based strategy testing.

• Score the potential price impact of news and announcements
• Organize historical events into a standard CSV file
• Combine event assessments with price-based trading signals

Assess whether sales and marketing are working by combining channel revenue, sales conversion, campaign returns, product performance, and customer feedback. Get a concise growth report with prioritized actions supported by available business data.

• Check whether marketing is producing sales
• Find where potential customers drop out of the sales process
• Review strong and weak product sales

Bring together public reviews, complaints, and customer activity to identify recurring issues and customers who may leave. Produce evidence-backed themes, review reply drafts in the owner's voice, and personalized messages to win back inactive customers.

• Finding recurring issues across reviews and complaints
• Drafting replies to positive and negative reviews
• Identifying customers whose ordering has slowed

Maps people involved in a deal or account using CRM records, communications, or supplied files. Shows roles, engagement, and stance with evidence, identifies missing decision makers and dependence on one contact, and suggests paths to new relationships.

• Identify decision makers and influencers in a deal
• Check whether a deal depends on a single contact
• Find introduction paths to people you have not reached

Analyze customer recommendation and satisfaction surveys, NPS and CSAT, from Google Sheets. Group comments into themes, explain weekly score changes and low-score drivers, and post a Slack digest with representative quotes without modifying survey data.

• Summarizing customer surveys each week
• Identifying recurring complaints behind low scores
• Comparing NPS or CSAT with the previous period

Investigate sharp stock price moves through company events, regulation, competitors, and market sentiment. Produce an event timeline and evidence-based explanations, showing whether the investment case needs review and where information remains missing.

• Investigate a sudden stock price rise or fall
• Understand market reactions after earnings
• Assess whether a headline matters

Build text, image, and audio features in JavaScript using machine learning models that run in browsers or server runtimes. Covers model selection, execution settings, caching, and memory cleanup without requiring a Python server.

• Add sentiment analysis to a JavaScript app
• Build image classification in a browser
• Implement audio transcription

Assess a brand’s presence on YouTube, Reddit, Wikipedia, LinkedIn, and other sources relevant to AI search. Review mentions, official profiles, and discussion sentiment, then produce a weighted authority score and recommendations for each platform.

• Reviewing brand mentions across major platforms
• Assessing positive and negative Reddit discussions
• Checking Wikipedia and Wikidata presence

Classify messages, rate content, or estimate probabilities using defined question types. Receive machine-readable answers, with confidence scores for classification and ratings, and apply the same questions to multiple inputs in a batch.

• Routing customer inquiries to departments
• Rating support quality against defined criteria
• Classifying sentiment across multiple messages

Build and deploy TypeScript agents on OpenServ that run custom tools, call APIs, or generate content. The skill covers capability definitions, platform registration, local development, task logging, and deployment, including platform-managed AI calls.

• Build an agent that calls external APIs
• Create a translation or sentiment-analysis agent
• Attach logs and generated files to tasks