Sentiment & Content Analysis - PowerPoint PPT Presentation

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Sentiment & Content Analysis

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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. – PowerPoint PPT presentation

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Title: Sentiment & Content Analysis


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
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3
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.

4
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 whats spoken in a
    video, even when the audio quality is
    inconsistent.

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6
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.

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8
About us
  • Aya Data provide fully managed annotation
    services at scale to build better computer
    vision-based AI. Whether its 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.

9
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