Caroline Brun is in charge of R&D projects in the area of natural language processing (NLP). She holds a PhD in computational linguistics. She has been working on parsing techniques, named entity recognition, word sense disambiguation. Now, her main research interests include sentiment analysis, in particular aspect-based sentiment analysis, emotion detection and personal language analytics. She has published many articles in international journals and conferences. She also holds several patents in the field and is a regular member of reviewing committees of international conferences.
Capturing Mixed Feelings with Fine Grained Sentiment Analysis
The World Wide Web has become a global forum where people share their feelings about almost everything. Social media networks spread vast amounts of user-generated content, where millions of people’s opinions are openly accessible. This content is of great value to policy makers, social scientists, and businesses. Due to the sheer quantity of information and the diversity of comments, managing brand reputation and customer relations will increasingly rely on technology that can automatically and reliably detect not simply binary opinions, but also, more subtle, nuanced sentiments and mixed feelings. While most of the work in sentiment analysis has primarily focused on classifying the overall polarity of user-generated documents, opinions or sentiments are usually not one-dimensional but multi-dimensional. People often care differently about different characteristics and features of products and services. There is a real need for organizations to understand what these specific sentiments are so they can pinpoint and prioritize what to do e.g. change features, improve service or communicate differently. Aspect Based Sentiment Analysis (ABSA) is precisely about mining text and summarizing the opinions expressed to ascertain the attitude of a speaker or writer toward specific entities (things) and their aspects. The Xerox Research Centre Europe is exploring this challenging topic using a combination of advanced natural language analysis with machine learning algorithms. We will briefly describe the topic, related challenges and share our work on multilingual ABSA, including our participation to the SemEval 2016 international Challenge in ABSA. We are currently looking to pilot the system with partners.