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Sentiment & Content Analysis

At Aya Data, we focus on providing bespoke, high quality data sets for our clients. Applying leading-edge methodologies for data annotation and labeling, we enable organizations to deploy AI systems exactly as they need to to achieve their target outcomes, cost-effectively and within the right timeframe.

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Sentiment & Content Analysis

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  1. Sentiment & 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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. • In 2021, we founded our company to address this imbalance and created opportunities where talent already exists. We named ourselves Aya after the Adinkra symbol for resourcefulness and endurance, a reflection of our team who have found a way to succeed in a complex industry, and who never give up on a challenge.

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