Talking in Time: The development of a self-administered conversation analysis based training programme for cochlear implant users.

Objectives: Training software to facilitate participation in conversations where overlapping talk is common was to be developed with the involvement of Cochlear implant (CI) users. Methods: Examples of common types of overlap were extracted from a recorded corpus of 3.5 hours of British English conversation. In eight meetings, an expert panel of five CI users tried out ideas for a computer-based training programme addressing difficulties in turn-taking. Results: Based on feedback from the panel, a training programme was devised. The first module consists of introductory videos. The three remaining modules, implemented in interactive software, focus on non-overlapped turn-taking, competitive overlaps and accidental overlaps. Discussion: The development process is considered in light of feedback from panel members and from an end of project dissemination event. Benefits, limitations and challenges of the present approach to user involvement and to the design of self-administered communication training programmes are discussed. Conclusion: The project was characterized by two innovative features: the involvement of service users not only at its outset and conclusion but throughout its course; and the exclusive use of naturally occurring conversational speech in the training programme. While both present practical challenges, the project has demonstrated the potential for ecologically valid speech rehabilitation training.

Beyond opinion classification: Extracting facts, opinions and experiences from health forums.

INTRODUCTION: Surveys indicate that patients, particularly those suffering from chronic conditions, strongly benefit from the information found in social networks and online forums. One challenge in accessing online health information is to differentiate between factual and more subjective information. In this work, we evaluate the feasibility of exploiting lexical, syntactic, semantic, network-based and emotional properties of texts to automatically classify patient-generated contents into three types: "experiences", "facts" and "opinions", using machine learning algorithms. In this context, our goal is to develop automatic methods that will make online health information more easily accessible and useful for patients, professionals and researchers. MATERIAL AND METHODS: We work with a set of 3000 posts to online health forums in breast cancer, morbus crohn and different allergies. Each sentence in a post is manually labeled as "experience", "fact" or "opinion". Using this data, we train a support vector machine algorithm to perform classification. The results are evaluated in a 10-fold cross validation procedure. RESULTS: Overall, we find that it is possible to predict the type of information contained in a forum post with a very high accuracy (over 80 percent) using simple text representations such as word embeddings and bags of words. We also analyze more complex features such as those based on the network properties, the polarity of words and the verbal tense of the sentences and show that, when combined with the previous ones, they can boost the results.