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What AI can do for multi-item sentiment analysis
Sentiment analysis assigns a positive, negative or neutral polarity to an item or entity, extracting and aggregating individual opinions from their textual expressions by means of natural language processing tools. It then aggregates the individuals' opinions into a collective sentiment about the item under consideration.
Current sentiment analysis techniques are satisfactory in case there is a single entity under consideration, but can lead to inaccurate or wrong results when dealing with a set of possibly correlated items. This can be useful, for example, when a company wants to know the collective preference order over a set of products. Or also when we want to predict the outcome of an election over a collection of candidates. We argue that, in order to deal with this more general setting, we should exploit AI techniques such as those used in preference reasoning, multi-agent systems, and computational social choice. Preference modelling and reasoning tools provide useful ingredients to model individuals' preferences in the most faithful way, while computational social choice techniques give methods to aggregate such preferences which satisfy certain desired properties. Other AI techniques can be very useful as well, such as machine learning or recommender system tools to cope with incompleteness in the information provided by each individual.
We describe a social choice aggregation rule which combines individuals' sentiment and preference information. We show that this rule satisfies a number of properties which have a natural interpretation in the sentiment analysis domain, and we evaluate its behavior when faced with highly incomplete domains.
This is joint work with Andrea Loreggia, Umberto Grandi, and Vijay A. Saraswat.
Bio: Francesca Rossi is Professor of Computer Science at the University of Padova, Italy. She is well known for her research contributions in areas such as constraint reasoning, preference handling, and computational social choice, and she is one of the editors of the Handbook of Constraint Programming. She is an ECCAI and a AAAI Fellow and currently serves as President of the IJCAI Board of Trustees.
For more information, please refer to her website at www.math.unipd.it.