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.
Obat Aborsi Pasuruan 0851\7696\3835 Jual Obat Cytotec Di Pasuruan
Sentiment & Content Analysis
1. 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.
3. 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. 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.
5.
6. 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.
7.
8. 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.
