Towards fine-grained frame-based sentiment analysis

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• Josef Ruppenhofer, University of Hildesheim
• Thursday 14 November 2013, 16:00-17:00
• UG05, Learning Centre.

If you have a question about this talk, please contact Christine Zarges.

Host: Andrew Gargett

In this presentation, I will discuss what FrameNet and Frame Semantics can offer sentiment analysis. In particular, I’ll be interested in fine-grained analysis of individual sentiment-bearing expressions. This is relevant for, e.g., opinion Q&A or summarization where we do not merely want to know, is this document (mostly) objective or neutral, or is it positive or negative in tone, but we want to know specifically who [source] feels how strongly [intensity] positive or negative [valence/(polarity] about what [target]?

First and foremost, FrameNet allows piggy-backing a good deal of the source and target identification problem onto the task of (automatic) semantic role labeling. FrameNet covers many emotion- and communication-related frames that are necessary to do this work. Furthermore, FrameNet contains not only senses of single words but also of multi-word expressions. It even accommodates syntactic patterns and constructions through the FrameNet constructicon. In addition, the FrameNet knowledge base already has positive/negative markings on particular (antonymous) lexical units that indicate polarity/valence.

However, some types of information are missing from the FrameNet knowledge base, but could be added. For instance, we would like to add information on scalar intensity, indicating, for instance that “handsome” < “gorgeous”. I will show possible extensions to FrameNet’s representation to accommodate these new types of information; discuss ways to acquire this information semi-automatically; and illustrate some specific benefits of what you can do with this additional information in hand.

This talk is part of the Computer Science Departmental Series series.

This talk is included in these lists:

• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• UG05, Learning Centre
• computer sience

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