SENTAL

Sentiment Analysis for User Generated Discourse in eLearning 2.0

Motivation

One prominent feature of eLearning 2.0 is collaboration. Members interact, learn

and share their opinions by creating mass amount of discourse through wikis, blogs and

forums. However, this growing amount of user generated discourse places considerable

burdens on learners as well as instructors who wish to track learners' opinions and views

on diverse topics or search for content containing opinions.

Goals

Enabling subjectivity and sentiment analysis for generating feedback from user generated discourse and for supporting information search in eLearning 2.0:

• investigate knowledge- and corpus-based methods for subjectivity and sentiment analysis
• determine the semantic orientation and strength of the opinions
• identify the targets of the opinions
• identify the holders of the opinions

System Architecture

Data

Darmstadt Service Review Corpus

People

• Prof. Iryna Gurevych, Principal Investigator
• Dipl.-Inf. Cigdem Toprak, Doctoral Researcher