Szociálpszichológiai és Interkulturális Pszichológiai Tanszék

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    Temporal structure of narratives reveals the intensity of the narrator’s current affective state
    (2021) Pólya Tibor
    Remembering on past emotional episodes frequently elicits an affective state in the remembering person, and remembering in a social context is usually accompanied by narration. This study considers the relationship between the narrator’s affective state and the structure of narratives. More specifically, the study addressees the question whether the temporal structure of narratives reveals the intensity of narrators’ current affective state. The study included 75 participants. They were asked to recount past emotion episodes applying a cue word paradigm with the following emotion category labels: anger, sadness, joy, and pride. Intensity of the narrators’ current affective state was assessed by physiological and self-report measures. The temporal structure of narratives was reflected by the two features of specific temporal reference and temporal unfolding. These features were coded by the method of automated linguistic analysis. The results show that specific temporal reference reflects affective intensity measured as the level of arousal while temporal unfolding reflects affective intensity measured as the valence of the narrator’s current affective state. Results are discussed by highlighting the function of temporal structure of narratives in reliving past experiences during narration.
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    Narrative Construction of Product Reviews Reveals the Level of Post-Decisional Cognitive Dissonance
    (2021) Pólya Tibor; Kengyel Gabriella Judith; Budai Tímea
    Social media platforms host an increasing amount of costumer reviews on a wide range of products. While most studies on product reviews focus on the sentiments expressed or helpfulness judged by readers and on their impact on subsequent buying this study aims at uncovering the psychological state of the persons making the reviews. More specifically, the study applies a narrative approach to the analysis of product reviews and addresses the question what the narrative construction of product reviews reveals about the level of post-decisional cognitive dissonance experienced by reviewers. The study involved 94 participants, who were asked to write a product review on their recently bought cell phones. The level of cognitive dissonance was measured by a self-report scale. The product reviews were analyzed by the Narrative Categorical Content Analytical Toolkit. The analysis revealed that agency, spatio-temporal perspective, and psychological perspective reflected the level of cognitive dissonance of the reviewers. The results are interpreted by elaborating on the idea that narratives have affordance to express affect.
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    Emotion Recognition Based on the Structure of Narratives
    (2023) Pólya Tibor; Csertő István
    One important application of natural language processing (NLP) is the recognition of emotions in text. Most current emotion analyzers use a set of linguistic features such as emotion lexicons, n-grams, word embeddings, and emoticons. This study proposes a new strategy to perform emotion recognition, which is based on the homologous structure of emotions and narratives. It is argued that emotions and narratives share both a goal-based structure and an evaluation structure. The new strategy was tested in an empirical study with 117 participants who recounted two narratives about their past emotional experiences, including one positive and one negative episode. Immediately after narrating each episode, the participants reported their current affective state using the Affect Grid. The goal-based structure and evaluation structure of the narratives were analyzed with a hybrid method. First, a linguistic analysis of the texts was carried out, including tokenization, lemmatization, part-of-speech tagging, and morphological analysis. Second, an extensive set of rule-based algorithms was used to analyze the goal-based structure of, and evaluations in, the narratives. Third, the output was fed into machine learning classifiers of narrative structural features that previously proved to be effective predictors of the narrator’s current affective state. This hybrid procedure yielded a high average F1 score (0.72). The results are discussed in terms of the benefits of employing narrative structure analysis in NLP-based emotion recognition.