Predicting Influence of Online Reviews with Emotion Extraction [Archived]

June 27, 2014
Duration: One semester (can be adapted to both bachelor and master students)
Goals: Improve our prediction capability of influence among reviews from e-Commerce websites, and our understanding of human behavior in this setting.
Responsible TA: Lionel Martin  and Pearl Pu
Keywords: emotion recognition, influence detection, prediction, social media analysis, natural language processing
Abstract:

Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted yet but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length of review or its readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners’ attention, we believe that they can serve as important parameters for predicting helpfulness of review texts. This is an excellent opportunity to develop a combined approach of Machine Learning and Natural Language Processing to improve the prediction of these influential reviews and provide a light interface to users.

The responsible student will:

  1. Familiarize with existing techniques in influence prediction and emotion extraction
  2. Discover and try the existing framework for prediction
  3. Develop new learning mechanisms to extract emotions from review texts
  4. Understand the features of interest in the prediction process
Related Skills: Programming skills (Python, Matlab or similar for prediction); Interest in Machine Learning, Natural Language Processing, and/or Text Mining; French or English mother tongue a plus