Detect positive, negative, and neutral sentiment across customer calls, research interviews, survey responses, and any text or media data. Speak analyzes emotional tone automatically, identifies speaker-level sentiment, and tracks how attitudes shift across conversations and datasets over time.
Upload audio, video, or text from any source. Speak connects with Zoom, Teams, Meet, and thousands of workflows via Zapier for automatic sentiment analysis.
Most sentiment tools only handle text. Speak detects sentiment in audio recordings, video content, and written text, then gives you dashboards, AI Chat, and exports to act on what you find.
Detect tone, emotion, and attitude in recorded conversations. Speak transcribes your audio and analyzes sentiment at the sentence level, so you can identify where calls go positive or negative and pinpoint the moments that matter most.
Analyze sentiment from video content with full speaker context. Upload recorded interviews, focus groups, or presentations and Speak processes the audio track, identifies speakers, and maps sentiment across the entire conversation timeline.
Process survey responses, customer reviews, support tickets, and any written feedback at scale. Paste or upload text data and Speak classifies sentiment automatically, giving you a structured view of attitudes across thousands of responses.
Track how sentiment shifts across a single conversation or an entire dataset. See where a customer call turns from frustration to resolution, or how participant attitudes evolve across a multi-week research study. Visualize trends with built-in charts.
Compare sentiment between different speakers in the same conversation. Understand how each participant feels throughout a call, identify who expressed concerns, and see how interviewer questions influenced respondent attitudes.
Ask questions like “When did sentiment turn negative in this call?” or “Which interviews had the most positive responses about the product?” AI Chat lets you query sentiment patterns across individual files or your entire library using Claude, Gemini, or GPT.
Understand which topics drive positive or negative responses. Speak connects keyword extraction with sentiment scores, so you can see that mentions of “pricing” correlate with negative sentiment while “onboarding” skews positive across your dataset.
Visualize sentiment trends with charts, comparisons, and aggregate views. See overall sentiment distribution across a project, compare sentiment between different speaker groups, and track how attitudes shift week over week or month over month.
Download sentiment results as CSV, generate reports, or connect via API for downstream analysis. Bring Speak’s sentiment data into your BI tools, research platforms, or custom dashboards. Every data point is exportable and structured for integration.
Researchers, CX teams, sales leaders, and analysts use Speak to understand emotional tone and attitudes across conversations and text data. Here is how sentiment analysis drives better decisions.
Detect frustration, satisfaction, and churn signals across support and success calls. Identify which calls need immediate follow-up, track sentiment trends over time, and give your team data-driven visibility into how customers actually feel about your product.
Measure participant attitudes and emotional responses across qualitative interviews. Instead of relying on subjective impressions, use automated sentiment scoring to identify which topics generated the strongest reactions and compare responses across participants.
Process open-ended survey responses at scale without reading every answer individually. Speak classifies sentiment across thousands of text responses, surfaces the most positive and negative comments, and gives you aggregate sentiment views by question or segment.
Identify where deals go positive or negative during sales conversations. Track sentiment patterns across your team’s calls to understand what top performers do differently, spot objection moments, and build coaching programs based on real conversation data.
Track sentiment in brand mentions, media coverage, podcast appearances, and press interviews. Upload or transcribe media content and see how your brand is being discussed, whether coverage skews positive or negative, and how sentiment changes over time.
Analyze sentiment in feedback sessions, town halls, and exit interviews. Understand how employees feel about leadership decisions, company culture, and workplace changes without relying solely on structured survey scores that miss the nuance of real conversations.
Dedicated sentiment tools handle text only. General transcription tools skip sentiment entirely. Speak combines transcription, multi-source sentiment analysis, and flexible AI in one platform built for teams that work with real conversation data.
Analyze sentiment across audio, video, and text in one platform. Most sentiment tools are text-only, which means you need to transcribe recordings separately before you can analyze them. Speak handles the full pipeline from raw media to sentiment insights without extra steps or tools.
Choose Claude, Gemini, or GPT for sentiment tasks. Different models handle nuance, sarcasm, and cultural context differently. Speak gives you the flexibility to pick the model that performs best for your specific data type and analysis goals.
Simple polarity scoring misses the complexity of real conversations. Speak detects nuanced emotional signals across conversations, helping you understand not just whether someone is positive or negative, but how their attitude shifts throughout a discussion and why.
Track sentiment patterns across months of data, multiple projects, or different teams. Compare sentiment distributions between customer segments, monitor how product changes affect user attitudes, and build longitudinal views that single-file analysis tools cannot provide.
No separate step to get from recording to sentiment. Speak transcribes your audio and video with speaker labels, then runs sentiment analysis on the transcript automatically. You upload a file and get sentiment results without stitching together multiple tools.
Set up AI Agents to automatically process incoming recordings, run sentiment analysis, generate summary reports, and distribute findings to your team. Instead of manually reviewing every call or interview, let agents handle the repetitive analysis work.
Skapa ett gratis Speak-konto and upload your files directly, paste text content, or connect your calendar for automatic meeting recording. Speak accepts all major audio and video formats along with text documents and CSV files.
Audio and video files are transcribed with speaker labels using your choice of transcription engine. Text data is ingested directly. All content is prepared for NLP analysis without any manual preprocessing on your end.
Speak runs sentiment analysis across your content, scoring positive, negative, and neutral sentiment at the sentence level. Speaker-level aggregation shows how each participant’s attitudes compare, and temporal analysis reveals how sentiment shifts throughout a conversation.
View sentiment distributions, trends over time, and keyword-sentiment correlations in your analytics dashboard. Use AI Chat to ask questions like “Which calls had the most negative sentiment about pricing?” or “How did participant attitudes change between session one and session three?”
Download sentiment data as CSV, generate reports, or share insights with your team through Speak’s collaboration features. Set up automated exports via Zapier or use the API to feed sentiment data into your existing analytics stack.
Sentiment analysis is the process of identifying and classifying the emotional tone behind a piece of text, audio, or video content. At its simplest, sentiment analysis classifies content as positive, negative, or neutral. At its most advanced, it detects nuanced emotional signals, tracks how attitudes shift over time, and connects sentiment patterns to specific topics, speakers, and business outcomes.
For businesses and researchers, sentiment analysis has moved from a nice-to-have to a core part of how they understand customers, participants, and markets. Customer experience teams use it to monitor call sentiment and detect churn risk. Researchers use it to measure participant attitudes across dozens of interviews. Product teams use it to understand how users feel about feature changes. The common thread is that sentiment analysis turns subjective human reactions into structured, measurable data.
Early sentiment analysis tools relied on keyword-based rules and simple scoring dictionaries. A word like “terrible” would score negative, “great” would score positive, and the tool would average out the scores. This approach missed sarcasm, context, and the complex ways people actually express opinions in conversation.
Modern AI-powered sentiment analysis uses large language models that understand context, tone, and nuance. These models can detect that “This is just great” might be sarcastic depending on surrounding context. They handle negation, hedging, and mixed sentiment within a single sentence. And they work across languages, making multilingual sentiment analysis practical for global teams. Tala gives you access to Claude, Gemini, and GPT for sentiment tasks, so you can choose the model that handles your specific data best.
Most sentiment analysis tools were built for text only. You paste in tweets, reviews, or survey responses and get a polarity score. But the richest sentiment signals often live in conversations, not written text. The way someone’s voice shifts during a customer call, the moment a research participant hesitates before answering, the tone change when a sales prospect hears your pricing. These signals are lost when you only analyze text.
Speak is built for teams that need sentiment analysis across audio, video, and text. Instead of transcribing recordings in one tool and running textanalys in another, you upload your media and get transcription plus sentiment analysis in a single workflow. This matters because it removes friction. The fewer steps between raw data and insights, the more likely your team will actually use the tool consistently.
The value of sentiment analysis is not the sentiment score itself. It is the decisions you make based on that score. When a CX team sees that negative sentiment spikes during pricing discussions across 200+ calls, that is a signal to revisit pricing communication. When a researcher sees that participants consistently express frustration about a specific workflow step, that is actionable product feedback. When a sales leader sees that top performers maintain positive sentiment 40% longer in calls than average reps, that becomes a coaching opportunity.
Speaks AI-agenter make this even more practical by automating sentiment monitoring. Instead of manually reviewing every recording, you can set up agents to flag calls where negative sentiment exceeds a threshold, generate weekly sentiment reports across your team’s conversations, or alert you when sentiment trends shift in a specific direction.
When evaluating sentiment analysis tools, consider how your team actually works with data. If you only analyze text, a text-only tool may suffice. But if your data includes call recordings, research interviews, video content, or a mix of media types, you need a platform that handles the full pipeline from raw recording to sentiment insight. Look for speaker-level analysis, temporal sentiment tracking, the ability to query results with AI, and export options that fit your existing workflows. Speak is built for that second category: teams that need sentiment analysis across every type of conversation and text data they collect.
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Common questions about sentiment analysis, how AI sentiment detection works, and how Speak handles audio, video, and text sentiment.
Sentiment analysis is the process of using natural language processing to identify and classify the emotional tone of text, audio, or video content. It typically classifies content as positive, negative, or neutral, though more advanced tools detect nuanced emotions and track how sentiment shifts over time. Businesses use sentiment analysis to understand customer attitudes, measure participant reactions in research, monitor brand perception, and identify patterns across large volumes of conversation data.
AI sentiment analysis uses large language models trained on vast amounts of text to understand context, tone, and meaning. Unlike older keyword-based approaches that simply counted positive and negative words, modern AI models understand sarcasm, negation, hedging, and mixed sentiment within the same sentence. Speak gives you access to Claude, Gemini, and GPT models for sentiment tasks, so you can choose the model that best handles your specific data type and language.
Yes. Speak handles sentiment analysis across audio, video, and text data in a single platform. For audio and video files, Speak first transcribes the content with speaker labels, then runs sentiment analysis on the transcript at the sentence and speaker level. This means you can upload a recorded customer call, research interview, or focus group and get sentiment insights without needing a separate transcription tool. Text content like survey responses and reviews can be uploaded or pasted directly.
Accuracy depends on the quality of the input data, the language, and the complexity of the content. Modern AI models handle straightforward sentiment very well and are increasingly capable with sarcasm, mixed emotions, and cultural nuance. Speak improves accuracy by offering multiple AI models, so you can test which model performs best for your specific use case. For audio and video data, transcription quality also matters, which is why Speak offers multiple transcription engines optimized for different recording conditions.
Yes. Speak provides temporal sentiment analysis at two levels. Within a single recording or document, you can see how sentiment shifts from start to finish, identifying the exact moments where a conversation turned positive or negative. Across a dataset, you can track aggregate sentiment trends over weeks or months to monitor how customer attitudes, participant reactions, or employee engagement evolve over time. Both views are available in the analytics dashboard.
Yes. While basic polarity scoring (positive, negative, neutral) is the foundation, Speak’s AI models can identify more nuanced emotional signals in conversations. Using AI Chat, you can ask specific questions about emotional tone, hesitation, enthusiasm, frustration, or confidence across your data. The multi-model approach means you can leverage different AI strengths for different types of emotional analysis.
Yes. Speak supports transcription and analysis in multiple languages. The AI models available through Speak handle sentiment analysis across major world languages, making it practical for global research teams, multinational customer experience programs, and any workflow that involves multilingual data. Transcription engine selection also matters for multilingual accuracy, and Speak offers engine options optimized for different languages.
Dedicated sentiment analysis tools like MonkeyLearn, Lexalytics, or IBM Watson NLU are built for text-only analysis and often require technical integration. Speak is different in three ways. First, it handles audio and video natively, so you do not need a separate transcription pipeline. Second, it provides a complete analysis platform with dashboards, AI Chat, and collaboration features rather than just an API. Third, it offers multi-model AI so you are not locked into a single provider’s sentiment model. For teams that work with conversation data and not just text, Speak provides a more complete workflow.
Upload your audio, video, or text data and get sentiment analysis in minutes. Track attitudes over time, compare sentiment across speakers and datasets, and use AI Chat to explore patterns across your entire library.
Create a free account, upload your first recording or text file, and see sentiment results immediately. Get full access to dashboards, AI Chat, and exports during your 7-day trial.
Need help setting up sentiment analysis workflows for your organization? We help teams configure automated analysis pipelines, integrate with existing tools, and build custom reporting. Book a consult to get started. Explore all of Speak’s AI tools for audio files, including transcription, keyword extraction, and theme detection alongside sentiment analysis.
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