The DIVIDED SELF metaphor and conceptualizations of the internal conflict in suicide notes

This paper presents DIVIDED SELF metaphor analysis conducted drawing from the discourse of suicidal notes. The suicide notes represent a distinct genre because of its typical rhetorical structure and communicative purpose. In particular, the internal conflict experienced by the authors of suicidal notes makes this material suitable for an analysis of the metaphorical conceptualization of one’s own DIVIDED SELF. The research aims at modeling the conceptualization of one’s own conflicting SELF by the authors of the suicide notes and proposing approach to the metaphorical conceptualizations of the DIVIDED SELF as metaphtonymy, as well as describing their potential for representing the individual’s internal conflict. First, the cognitive framing of the inner SELF of the authors, divided into the instances of the Subject and the Self, was investigated. Second, in order to analyze metaphtonymic connections between the individual’s inner conceptualizations, the agentivity of the inner SELF conceptualizations was compared. Third, a metaphtonymic configuration of SELF conceptualizations was modeled and the potential of metaphorical framing of extreme psychological states through the metaphthonymic representation of the SELF described. The material of the study consisted of a corpus with a total size of 164,483 lexical units (the CEASE corpus combined with a self-assembled corpus of suicide notes). As demonstrated by the analysis, the aspect of the Self mainly acts as a fragmentation of the author in the DIVIDED SELF metaphor. That is, the study allowed to model metaphorical conceptualizations metaphtonymically and structure the stages of the formation of metaphtonymy through the visual illustrations

A hybrid group-based movie recommendation framework with overlapping memberships

Recommender Systems (RS) are widely used to help people or group of people in finding their required information amid the issue of ever-growing information overload. The existing group recommender approaches consider users to be part of a single group only, but in real life a user may be associated with multiple groups having conflicting preferences. For instance, a person may have different preferences in watching movies with friends than with family. In this paper, we address this problem by proposing a Hybrid Two-phase Group Recommender Framework (HTGF) that takes into consideration the possibility of users having simultaneous membership of multiple groups. Unlike the existing group recommender systems that use traditional methods like K-Means, Pearson correlation, and cosine similarity to form groups, we use Fuzzy C-means clustering which assigns a degree of membership to each user for each group, and then Pearson similarity is used to form groups. We demonstrate the usefulness of our proposed framework using a movies data set. The experiments were conducted on MovieLens 1M dataset where we used Neural Collaborative Filtering to recommend Top-k movies to each group. The results demonstrate that our proposed framework outperforms the traditional approaches when compared in terms of group satisfaction parameters, as well as the conventional metrics of precision, recall, and F-measure