Sentiment & Content Analysis
Feb 16, 2022
Most data generated for Machine Learning models is voluminous and unstructured. It's time consuming and costly to annotate, validate and fine-tune data to a point where it can optimally train a machine learning model.
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Most data generated for Machine Learning models is voluminous and unstructured. It's time consuming and costly to annotate, validate and fine-tune data to a point where it can optimally train a machine learning model.
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Sentiment & Content AnalysisSentiment & Content Analysis
AI sentiment analysis uses natural language processing (NLP) techniques to recognise and classify emotions (positive, negative and neutral) in text and speech data. As your machine continuously learns to identify user sentiment towards your presence online, you can make evolving decisions for your brand, product development and customer engagement based on updated and better-structured data sets.
Content Analysis Processes
Content analysis processes text and audio-based messages into actionable, structured data sets. By assessing messages’ attributes through systematic, quantitative and objective, techniques, AI learns to perform deep analysis and labelling of their contents.
Text-based messages may include published articles, news headlines, social media posts and blog commentary, while audio includes recorded files and online radio.
Audio & Text Transcription
Once your AI has optimized its language processing and learnt to analyze, categorize and store data sets based on audio and speech, it can transcribe these files into accurate, shareable text.
With accurate transcription, users have more control over how they consume your content. They can share soundbites from a podcast as social media messages, or understand what’s spoken in a video, even when the audio quality is inconsistent.
Named Entity Recognition
Understanding language begins with identifying and categorising specific tokens within unstructured text.
Through Named Entity Recognition (NER), a natural language processing (NLP) method, machines can automatically recognise and predict named entities in text and speech, according to predefined data categories. Sample entities may include names, locations, businesses, objects, quantities or percentages.
About us
Aya Data provide fully managed annotation services at scale to build better computer vision-based AI. Whether it’s Geospatial Analytics, Autonomous Vehicles or Robotics we create bespoke datasets to fine-tune your ML models.
AI sentiment analysis uses natural language processing (NLP) techniques to recognise and classify emotions (positive, negative and neutral) in text and speech data. As your machine continuously learns to identify user sentiment towards your presence online, you can make evolving decisions for your brand, product development and customer engagement based on updated and better-structured data sets.
Content Analysis Processes
Content analysis processes text and audio-based messages into actionable, structured data sets. By assessing messages’ attributes through systematic, quantitative and objective, techniques, AI learns to perform deep analysis and labelling of their contents.
Text-based messages may include published articles, news headlines, social media posts and blog commentary, while audio includes recorded files and online radio.
Audio & Text Transcription
Once your AI has optimized its language processing and learnt to analyze, categorize and store data sets based on audio and speech, it can transcribe these files into accurate, shareable text.
With accurate transcription, users have more control over how they consume your content. They can share soundbites from a podcast as social media messages, or understand what’s spoken in a video, even when the audio quality is inconsistent.
Named Entity Recognition
Understanding language begins with identifying and categorising specific tokens within unstructured text.
Through Named Entity Recognition (NER), a natural language processing (NLP) method, machines can automatically recognise and predict named entities in text and speech, according to predefined data categories. Sample entities may include names, locations, businesses, objects, quantities or percentages.
About us
Aya Data provide fully managed annotation services at scale to build better computer vision-based AI. Whether it’s Geospatial Analytics, Autonomous Vehicles or Robotics we create bespoke datasets to fine-tune your ML models.
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