DATA SCIENCE

LE LAB

Computational Business Analytics

The project aims at constructing knowledge bases for business and management research using modern approaches; and machine learning frameworks for applying knowledge into optimization problems of social sciences, including logistics, marketing and finance.

    Project objectives:
  1. (sense) constructing knowledge base for business model and marketing research;
  2. (evaluate) evaluating business model using knowledge base;
  3. (respond) developing machine learning methods for customer behavior analysis;
  4. (respond) developing machine learning methods for time series prediction.

Text Analysis

This research concentrates on methods for text preprocessing, including text cleansing and filtering; term extraction, and downstream analysis like discussion topic identification, sentiment analysis.

    Research topics include
  1. Text cleansing and term extraction;
  2. Text clustering using term-term similarity;
  3. Text topic classification using keywords;
  4. Online market segmentation.
    Benchmark Datasets:
  1. Stanford Large Movie Review Dataset;
  2. Yelp Dataset Challenge;
  3. Amazon review dataset at UCSD;
  4. Stanford MemeTracker data.

Sentiment Analysis

This research aims at developing knowledge base methods for sentiment analysis using text messages and posts made available on public social media, including social networks, blogs, etc.

    Research topics:
  1. Lexicon creation and optimization;
  2. Developing hybrid method using lexicon based approach;
  3. Domain ontology creation and customization;
  4. Developing ontology based method for sentiment analysis.
    Benchmark Datasets:
  1. Stanford Large Movie Review Dataset;
  2. Yelp Dataset Challenge;
  3. Amazon review dataset at UCSD;
  4. Data for Everyone.

Business Model Analysis

This research aims at developing a knowledge base method for business model representation and application in business model validation/evaluation.

    Research topics include
  1. A knowledge base for business model corpus;
  2. Business model representation;
  3. Business model validation/evaluation;
  4. Online business model analysis.
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