A Semi-Automated Approach to the Content Analysis of Experience Narratives - Modeling Users' Experiences with Interactive Systems - page 113

Chapter 6
A Semi-Automated Approach to the Content
Analysis of Experience Narratives

Abstract. iScale will typically result in a wealth of experience narratives relating
to different stages of products' adoption. The qualitative analysis of these narrative
is a labor intensive, and prone to researcher bias activity. This chapter proposes a
semi-automated technique that aims at supporting the researcher in the content anal-
ysis of experience narratives. The technique combines traditional qualitative coding
procedures (Strauss and Corbin, 1998) with computational approaches for assess-
ing the semantic similarity between documents (Salton et al., 1975). This results
in an iterative process of qualitative coding and visualization of insights which en-
ables to move quickly between high-level generalized knowledge and concrete and
idiosyncratic insights. The proposed approach was compared against a traditional
vector-space approach for assessing the semantic similarity between documents, the
Latent-Semantic Analysis (LSA), using a dataset of a study in chapter 4. Overall,
the proposed approach was shown to perform substantially better than traditional
LSA. However, interestingly enough, this was mainly rooted in the explicit model-
ing of relations between concepts and individual terms, and not in the restriction of
the list of terms to the ones that concern particular phenomena of interest.

6.1 Introduction

The previous chapter proposed a survey technique for the elicitation of experience
narratives from a large sample of users. The researcher is then faced with an over-
whelming amount of idiosyncratic experience narratives. Each narrative may pro-
vide a rich insight into the experience and the context in which it takes place. How-
ever, generalized knowledge may also be gained from these experience narratives.
Such generalized knowledge may be reflected in questions like: how frequent is a
certain kind of experience, what is the ratio of positive versus negative experiences
and how does this compare to competitive products, how does the dominance of dif-
ferent product qualities fluctuate over time and what should we improve to motivate
prolonged use?