Emotional word use in informal dementia carers: A linguistic analysis of online discussion forums (Preprint)

BACKGROUND Growing numbers of informal dementia carers are turning to online discussion forums for support. Research has explored emotional word use on online discussion forums as a proxy for underlying emotional functioning. We are not aware of any research that has analysed the content of posts on discussion forums specific to dementia carers in order to examine their emotional states. OBJECTIVE We address the following research questions: 1) To what extent does emotional language use differ between dementia carers and non-carers? 2) To what extent does emotional language use differ between spousal and parental carers? 3) To what extent does emotional language use differ between current and former carers? METHODS We used the Linguistic Inquiry and Word Count programme to examine emotional word use on a UK-based online forum for informal dementia carers and a discussion forum control group. Carers were separated into different subgroups for the analysis: current and former; and spousal and parental. RESULTS We found that dementia carers used significantly more negative, but not positive, emotion words than non-carers. Spousal carers used more emotion words overall than parental carers, specifically more negative emotion words. Former carers used more emotional words overall than current carers, specifically more positive words. CONCLUSIONS The findings suggest that informal dementia carers may be at increased risk of negative emotional states, relative to non-carers. Greater negativity in spousal carers may be explained by increased caregiver burden, whereas greater positivity in former carers may be explained by functional relief of caregiving responsibilities. The theoretical/applied relevance of these findings is discussed.

Chinese Emotional Two-character Word Processing in Sentence Reading

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The Influence of Action Video Gaming Experience on the Perception of Emotional Faces and Emotional Word Meaning

Action video gaming (AVG) experience has been found related to sensorimotor and attentional development. However, the influence of AVG experience on emotional perception skills is still unclear. Using behavioral and ERP measures, this study examined the relationship between AVG experience and the ability to decode emotional faces and emotional word meanings. AVG experts and amateurs completed an emotional word-face Stroop task prior to (the pregaming phase) and after (the postgaming phase) a 1 h AVG session. With-group comparisons showed that after the 1 h AVG session, a more negative N400 was observed in both groups of participants, and a more negative N170 was observed in the experts. Between-group comparisons showed that the experts had a greater change of N170 and N400 amplitudes across phases than the amateurs. The results suggest that both the 1 h and long-term AVG experiences may be related to an increased difficulty of emotional perception. Furthermore, certain behavioral and ERP measures showed neither within- nor between-group differences, suggesting that the relationship between AVG experience and emotional perception skills still needs further research.

Learning emotional word embeddings for sentiment analysis

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

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