## 4 Use Cases for Multimedia Analysis in Archiving Community Memories
### Measuring Opinion and Sentiment  
The analysis of opinions and sentiment within a collection of community memories is often an important requirement of the end users of the archive. Specific examples include sentiment-based search (e.g., \"find me positive media about X\") and the determination of the key influential media objects (see the previous use cases of determining trends and events). Images are often used to illustrate the opinions expressed by the text of a particular article. By themselves, images also have the ability to convey and elicit opinions, emotions and sentiments. In order to investigate how images are used in the opinion formation process, we have been developing tools that: (1) allow the reuse of images within an archive to be explored with respect to diverse time and opinion axes; and (2) allow in-depth analysis of specific elements (in particular, the presence and expression of human faces) within an image to be used to quantify opinion and sentiment. Active shape models [51] (ASMs) and active appearance models [52] (AAMs) are well-known algorithms for fitting a shape to an image using the image's gradients to choose the best position for the vertices of the shape. As these models are parametric and generative (they can be used to reconstruct the shape from a small number of parameters), a large range of poses, expressions and appearances (e.g., skin textures) can be generated. Fitting a model to an image is a constrained optimisation problem in which the parameters of the model are iteratively updated in order to minimise the difference between the generated model and the image. Once a model is fitted to an image, the parameters can then be used as input to an expression classifier that can determine an expression label for the face. This model fits well with the Facial Action Coding System (FACS) [53], which aims to provide a standardised way of describing the expressions of faces. Codes represent muscular actions in the face (such as \"inner eyebrow raising\" or \"lip corner puller\"), and when combined, they represent emotions (for example, activation of the lip corner puller AU6 (_'Action Unit number 6'_) and the cheek raiser AU12 actions imply happiness). These muscular movements map to combinations of parameters in the face model, so a classifier can be trained on the model to recognise these actions. Of course, this relies on accurate face model fitting. Unfortunately, it is difficult to build a model that will accurately fit all faces and poses, which is essential for the accurate measurement of the shape parameters needed for expression classification. Another problem is that accurate detection of a face is required to initialise the fitting of a face model; whilst face detection techniques are quite mature, they can still have major problems working on real-world images, where the faces are not exactly frontal to the camera or there are shadows or contrast issues. Our research in ARCOMEM has shown that there is some promise in using these kinds of approaches, but they are not yet mature enough to be applied to most real-world imagery. A screenshot of our experimental system working under laboratory conditions is shown in Figure 7.  
In less constrained visual media, we cannot rely on there being faces in the images, and sentiment may be carried by other visual traits. Indeed, images may intrinsically have sentiment associated with them through design (for example, a poster for a horror film) or through association with a specific subject matter, which may be context sensitive (say a photo of wind generators in the context of climate change). For these situations, there are no specific algorithms that we can use for extracting the sentiment, and whilst predictions of the opinions and sentiment of visual content can be made by considering the visual content alone, a richer approach is to consider both the image and the context in which it appears.