The main objective of Information Retrieval (IR) systems is to satisfy searchers’ needs. A great deal of research has been conducted in the past to attempt to achieve a better insight into searchers’ needs and the factors that can potentially inﬂuence the success of an Information Retrieval and Seeking (IR&S) process.
One of the factors which has been considered is searchers’ emotion. It has been shown in previous research that emotion plays an important role in the success of an IR&S process which has the purpose of satisfying an information need. However, these previous studies do not give a sufﬁciently prominent position to emotion in IR, since they limit the role of emotion to a secondary factor, by assuming that a lack of knowledge (the need for information) is the primary factor (the motivation of the search).
In this thesis, we propose to treat emotion as the principal factor in the system of needs of a searcher, and therefore one that ought to be considered by the retrieval algorithms. We present a more realistic view of searchers’ needs by considering not only theories from information retrieval and science, but also from psychology, philosophy, and sociology. We extensively report on the role of emotion in every aspect of human behaviour, both at an individual and social level. This serves not only to modify the current IR views of emotion, but more importantly to uncover social
situations where emotion is the primary factor (i.e., source of motivation) in an IR&S process.
We also show that the emotion aspect of documents plays an important part in satisfying the searcher’s need, in particular when emotion is indeed a primary factor. Given the above, we deﬁne three concepts, called emotion need, emotion object and emotion relevance, and present a conceptual map that utilises these concepts in IR tasks and scenarios.
In order to investigate the practical concepts such as emotion object and emotion relevance in a real-life application, we ﬁrst study the possibility of extracting emotion from text, since this is the ﬁrst pragmatic challenge to be solved before any IR task can be tackled. For this purpose we developed a text-based emotion extraction system and demonstrate that it outperforms other available emotion extraction approaches.
Using the developed emotion extraction system, the usefulness of the practical concepts mentioned above is studied in two scenarios: movie recommendation and news diversiﬁcation.
In the movie recommendation scenario, two collaborative ﬁltering (CF) models were proposed. CF systems aim to recommend items to a user, based on the information gathered from other users who have similar interests. CF techniques do not handle data sparsity well, especially in the case of the cold start problem, where there is no past rating for an item. In order to predict the rating of an item for a given user, the ﬁrst and second models rely on an extension of state-of-the-art memory-based and model-based CF systems. The features used by the models are two emotion spaces extracted from the movie plot summary and the reviews made by users, and three semantic spaces, namely, actor, director, and genre. Experiments with twoMovieLens datasets show that the inclusion of emotion information signiﬁcantly improves the accuracy of prediction when compared with the state-of-the-art CF techniques, and also tackles data sparsity issues.
In the news retrieval scenario, a novel way of diversifying results, i.e., diversifying based on the emotion aspect of documents, is proposed. For this purpose, two approaches are introduced to consider emotion features for diversiﬁcation, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness.
Overall, this thesis shows that emotion plays a key role in IR and that its importance needs to be considered. At a more detailed level, it illustrates the crucial part that emotion can play in
* searchers, both as a primary (emotion need) and secondary factor (inﬂuential role) in an IR&S process;
* enhancing the representation of a document using emotion features (emotion object); and ﬁnally,
* improving the effectiveness of IR systems at satisfying searchers’ needs (emotion relevance).