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A corpus-based approach to finding happiness

Recipe for Happiness: Go shop for something new -- something cool, make sure that you love it. Then have lots of food, for dinner preferably, as the times of breakfast and lunch are to be avoided. Consider also including a new, hot taste, and one of your favorite drinks. Then go to an interesting place, it could be a movie, a concert, a party, or any other social place. Having fun, and optionally getting drunk, is also part of the recipe. Note that you should avoid any unnecessary actions, as they can occasionally trigger feelings of unhappiness. Ideally the recipe should be served on a Saturday, for maximum happiness effect. If all this happens on your birthday, even better. Bon appetit.

What would they think? a computational model of attitudes

A key to improving at any task is frequent feedback from people whose opinions we care about: our family, friends, mentors, and the experts. However, such input is not usually available from the right people at the time it is needed most, and attaining a deep understanding of someone else"s perspective requires immense effort. This paper introduces a technological solution. We present a novel method for automatically modeling a person"s attitudes and opinions, and a proactive interface called "What Would They Think?" which offers the just-in-time perspectives of people whose opinions we care about, based on whatever the user happens to be reading or writing. In the application, each person is represented by a "digital persona," generated from an automated analysis of personal texts (e.g. weblogs and papers written by the person being modeled) using natural language processing and commonsense-based textual-affect sensing.

Visualizing the affective structure of a text document

This paper introduces an approach for graphically visualizing the affective structure of a text document. A document is first affectively analyzed using a unique textual affect sensing engine, which leverages commonsense knowledge to classify text more reliably and comprehensively than can be achieved with keyword spotting methods alone. Using this engine, sentences are annotated using six basic Ekman emotions. Colors used to represent each of these emotions are sequenced into a color bar, which represents the progression of affect through a text document. Smoothing techniques allow the user to vary the granularity of the affective structure being displayed on the color bar. The bar is hyperlinked in a way such that it can be used to easily navigate the document.color as the mode of representation.

A model of textual affect sensing using real-world knowledge

ACM IUI "Outstanding Paper Award" Recipient. This paper presents a novel way for assessing the affective qualities of natural language and a scenario for its use. Previous approaches to textual affect sensing have employed keyword spotting, lexical affinity, statistical methods, and handcrafted models. This paper demonstrates a new approach, using large-scale real-world knowledge about the inherent affective nature of everyday situations (such as "getting into a car accident") to classify sentences into "basic" emotion categories.