loader
banner

img Sentiment Analytics 40%
Mad
80%
Happy
#Cloud Sentiment Analysis with AWS Play
img
ABOUT US

What is Sentiment Analysis?

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.

  • Polarity: if the speaker express a positive or negative opinion,
  • Subject: the thing that is being talked about
  • Opinion holder: the person, or entity that expresses the opinion.

Types of Sentiment Analysis

A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence, or feature/aspect level—whether the expressed opinion in a document, a sentence or an entity feature/aspect is positive, negative, or neutral.

img
Fine-grained Analysis

Advanced, "beyond polarity" sentiment classification looks, for instance, at emotional states such as "angry", "sad", and "happy

  • Polarity
img
Emotion detection

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns.

  • Positive
  • Neutral
  • Negative
img
Multilingual analysis

The enhancement of predictive web analytics calculates statistical probabilities of future events online.

  • Probability
STILL HAVE QUESTIONS?

Relax because we always will be here for you

img
What software do you use for text analysis?

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting.

Real Time Sentiment Analysis with Deep Learning?

There are many variations of passages of Lorem Ipsum available, but the majority have suffered alteration in some form, It is a long established.

Are there any available databases for affective models?

Predictive models often perform calculations during live transactions, for example, to evaluate the risk or opportunity of a given customer or transaction, in order to guide a decision. With advancements in computing speed, individual agent modeling systems have become capable of simulating human behaviour or reactions to given stimuli or scenarios.