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 hand-crafted 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. This commonsense approach has new robustness implications.Open Mind Commonsense was used as a real world corpus of 400,000 facts about the everyday world. Four linguistic models are combined for robustness as a society of commonsense-based affect recognition. These models cooperate and compete to classify the affect of text.Such a system that analyzes affective qualities sentence by sentence is of practical value when people want to evaluate the text they are writing. As such, the system is tested in an email writing application. The results suggest that the approach is robust enough to enable plausible affective text user interfaces.

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Thereis a growingrealization thatcomputer systemswillneed to be increasingly sensitiveto theircontext.Traditionally, hardwareand softwarewereconceptualized as input/output systems:systemsthattook input,explicitlygiven to themby a human,and actedupon thatinput aloneto producean explicitoutput.Now, this viewis seen as beingtoo restrictive. Smart computers,intelligent agentsoftware,and digital devicesof the futurewillhaveto operateon data thatare not explicitlygivento them,data that theyobserveor gatherfor themselves. These operationsmay be dependenton time,place, weather,userpreferences, or the historyof interaction. In otherwords,context.But what, exactly,is context?We look at perspectivesfrom softwareagents,sensors,and embedded devices,and also contrasttraditional mathematical and formalapproaches.We see how each treatsthe problemof contextand discussthe implications for designof contextsensitivehardwareand software. W e are in the middle of many revolutions in computers and communication technologies: ever faster and cheaper computers, software with more and more functionality, and embedded computing in everyday devices. Yet much about the computer revolution is still unsatisfactory. Faster computers do not necessarily mean more productivity. More capable software is not necessarily easier to use. More gadgets sometimes cause more complications. What can we do to make sure that the increased capability of our artifacts actually improves people's lives? Several subfields of computer science propose paths to a solution. The field of artificial intelligence tells us that making computers more intelligent will help. The field of human-computer interaction tells us that more careful user-centered design and testing of direct-manipulation interfaces will help. And indeed they will. But in order for these solutions to be realized, we believe that they will have to grapple with a problem that has previously been given short shrift in these and other fields: the problem of context. We propose that a considerable portion of what we call intelligence in artificial intelligence or good design in human-computer interaction actually amounts to being sensitive to the context in which the artifacts are used. Doing "the right thing" entails that it be right given the user's current context. Many of the frustrations of today's software-cryptic error messages, tedious procedures, and brittle behavior-are often due to the program taking actions that may be right given the software's assumptions, but wrong for the user's actual context. The only way out is to have the software know more about, and be more sensitive to, context. Many aspects of the physical and conceptual environment can be included in the notion of context. Time and place are some obvious elements of context. Personal information about the user is part of context: Who is the user? What does he or she like or dislike? What does he or she know or not know?

Navigating through online documents has become an increasingly common HeI task. This paper investigates alternative methods to improve user performance for browsing World Wide Web and other documents. In a task that involved both scrolling and pointing, we compared three input methods against the status-quo. The results showed that a mouse with a fmger wheel did not improve user's performance; two other methods, namely a mouse with an isometric rate-control joystick operated by the same hand and a two handed system that put a mouse on the dominant hand and a joystick on the other, both significantly improved users' performance. A human factors analysis on each of the three input methods is also presented.

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