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Mimicking human behaviour to achieve business goals

Artificial Intelligence (AI) / Machine Learning (ML) techniques have become the cornerstone of digital transformation. With myriad of use cases in every sector, it has become a subject of anticipation. It has the potential to transform businesses as well as the relationship between people and technology, at large. While AI is perceived to mimic human behavior, Machine Learning, a subset of AI, is more into analyzing human behavior, generate patterns and identify trends.

Why is AI/ML usage reaching critical mass today?

  • Crucial source of business value - when done right
  • Drives down the time taken to perform a task
  • Improves accuracy and decision-making
  • Operates 24x7 without interruption or breaks and has no downtime.
  • Fraud detection and predictive analytics

AI/ML Techniques


Sentiment Analysis

Sentiment Analysis is contextual mining of text which identifies and extracts subjective information in source material. Sentiment analysis with AI achieves something that wasn't previously possible: using machines to understand human emotions in text. Understand the social sentiment of your brand, product or service while monitoring online conversations.


Emotion Analysis

Sometimes the three classes of sentiment (positive, negative and neutral) are not sufficient to understand the nuances regarding the underlying tone of a sentence. AI emotion recognition is a field of computer vision focusing on facial emotion detection. It analyzes the proprietary dataset and tells whether the underlying emotion behind a message is: Happy, Sad, Angry, Fearful, Excited or Bored.


Keyword Extractor

Keyword Extractor is a powerful tool in text analysis that can be used to index data, generate tag clouds and accelerate the searching time using AI. It generates an extensive list of relevant keywords and phrases to make research more context focused.


Named Entity Recognition

Named Entity Recognition can identify individuals, companies, places, organization, cities and other various type of entities. Using AI/ML, API can extract this information from any type of text, web page or social media network.


Text Classification

Text Classification can be useful in understanding customer behavior by categorizing conversations on social networks, feedback and other web sources. Categorization done on the basis of information, query, complaint, appreciation and many more.


Intent Analysis

This classifier tells whether the underlying intention behind a sentence is opinion, news, marketing, complaint, suggestion, appreciation, and query. This is trained on our proprietary dataset.


Abusive Content Classifier

Protect abusive and offensive language in your forums or portals. System identifies offensive language with 98% accuracy and helps you in fighting online abuse and spam.


Opinion Mining

The science of using text analysis to understand the drivers behind public sentiment. It is a technique to know the ‘what’ and ‘why’ behind the public sentiment. An accurate synthesis is made possible using AI.

Process we follow at InfoBeans

The following describes how our API makes the best use of Artificial Intelligence to provide well-grained results:

Input relevant data

The input data goes through pre- processing where it gets filtered. The punctuations and links are removed and the data becomes more refined and relevant for the system.


After pre- processing, each word in the sentence is converted to their corresponding vectors(numeric representation), which are then fed to neural architecture.


These vectors are then passed through series of recurrent layers and then to the classification layer which generates the final output (sentiment). The output received is binary that corresponds to positive and negative context.


This output is then compared against the actual human tagged labels and then the error is calculated which is finally used to optimize the neural network through backpropagation. This process goes on until the network is optimized satisfactorily. Module is evaluated using confusion matrix to validate proper result sets.


Based on the input data set , our architecture has been trained to deliver an accuracy of 90%.


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