Business Objectives

  • Business data and information today may be available in the form of free flow text. Some examples of such data are customer feedback on social media, or employee resentment on company intranet portal or competitor information on various public portals.
  • While this information is quite valuable to the business, its difficult to process & consume such data in company’s business process.
  • Natural Language Processing techniques (NLP) and Deep Learning models such as Recurrent Neural Network (RNN) and Sequence-to-Sequence/LSTM models can be used to train Classifier models, which can help make sense of the free flow text data.

Methodology

Data Analysis

Study text data in detail.

Tokenize, Lemmatize, POS and other feature extraction techniques.

Assess type of model to be applied for best results

Text data is studied in detail. Text data is tokenized, lemmatized and other feature extraction activities are done. Feature extracted data is analyzed to look for patterns based on which decision is made on the right approach to meet the requirement

Model Training

Continue with Feature extraction.

Apply different models including RNN or primitive ML models.

Observe results on Train : Test.

Based on identified approach, more feature extraction is done one and AI models are trained. Based on patterns observed and volume of data, either deep learning models or normal Machine Learning model (such as Logistic regression, SVM etc.) or a combination is built. Trained models are tested on train:test split and results are published.

Present Results

Train Supervised Learning Models.

Build API wrapper and build a simple GUI to access model in programs.

Host the API and models on customer’s IT infrastructure.

Once the models are ready with desired results on train:test data, models are tested on blinded datasets to confirm the results. Models may be wrapped into a RESTful API and hosted on the hosting platform. A simple or complex GUI (based on agreed scope) may be created to invoke the API and see the results.

Deliverables

  • Results on Train:Test:Validation data sets.
  • RESTful API hosted on hosting platform.
  • A simple or complex GUI program integrated with the model API

Case Studies

  • Learn how neurIOT helps pharmaceutical companies score their marketing messages by applying heuristics and NLP techniques.
  • Learn how neurIOT helped a Police Department in a state in India to find criminals using NLP and Machine Learning techniques.