A multi-layered approach to surfacing and analysing organisational narratives : increasing representational authenticity

This paper presents an integrated, multi-layered approach to narrative inquiry, elucidating the evolving story of organisational culture through its members and their physical, textual, linguistic and visual dialogue. A dynamic joint venture scenario within the UK hi-technology sector was explored to advance understanding of the impact of transformation level change, specifically its influence on shared belief systems, values and behavioural norms. STRIKE – STructured Interpretation of the Knowledge Environment is introduced as an innovative technique to support narrative inquiry, providing a structured, unobtrusive framework to observe, record, evaluate and articulate the organisational setting. A manifestation of narrative in physical dialogue is illuminated from which the underlying emotional narrative can be surfaced. Focus groups were conducted alongside STRIKE to acquire a first order retrospective and contemporaneous narrative of culture and enable cross-method triangulation. Attention was given to non-verbal signals such as Chronemic, Paralinguistic, Kinesic and Proxemic communication and participants were also afforded opportunities to develop creative output in order to optimise engagement. Photography was employed to enrich STRIKE observation and document focus group output, affording high evidential value whilst providing a frame of reference for reflection. These tools enable a multiplicity of perspectives on narrative as part of methological bricolage. Rich, nuanced and multi-textured understanding is developed, as well as the identification of connections, timbre and subjugated knowledge. A highly emotional and nostalgic context was established with actors’ sense of self strongly aligned with the pre-joint venture organisation and its brand values, norms and expectations. Credibility and authenticity of findings is enhanced through data triangulation indicating traceability across methods, and from the contextual preservation attained through STRIKE. The multi-layered approach presented can facilitate researcher reflexivity and sense-making, while for the audience, it may be employed to help communicate and connect research findings. In particular, STRIKE demonstrates utility, quality and efficacy as a design artefact following ex-post evaluation. This systematic method of narrative inquiry is suitable for standardisation and alongside a diagnostic/prescriptive capacity, affords both researcher and practictioner value in its application

SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning

This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis, specifically in the context of Task 4 on the SemEval 2017 dataset. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations. Additionally, the study compares these GPT-based methodologies with other current, high-performing models previously used with the same dataset. The results illustrate the significant superiority of the GPT approaches in terms of predictive performance, more than 22\% in F1-score compared to the state-of-the-art. Further, the paper sheds light on common challenges in sentiment analysis tasks, such as understanding context and detecting sarcasm. It underscores the enhanced capabilities of the GPT models to effectively handle these complexities. Taken together, these findings highlight the promising potential of GPT models in sentiment analysis, setting the stage for future research in this field. The code can be found at https://github.com/DSAatUSU/SentimentGP

‘Ought Implies Can’: Not So Pragmatic After All

Those who want to deny the ‘ought implies can’ principle often turn to weakened views to explain ‘ought implies can’ phenomena. The two most common versions of such views are that ‘ought’ presupposes ‘can’, and that ‘ought’ conversationally implicates ‘can’. This paper will reject both views, and in doing so, present a case against any pragmatic view of ‘ought implies can’. Unlike much of the literature, I won't rely on counterexamples, but instead will argue that each of these views fails on its own terms. ‘Ought’ and ‘can’ do not obey the negation test for presupposition, and they do not obey the calculability or the cancelability tests for conversational implicature. I diagnose these failures as partly a result of the importance of the contrapositive of ‘ought implies can’. I end with a final argument emphasizing the role the principle plays in moral thinking, and the fact that no pragmatic account can do it justice

Knowledge-Driven Intelligent Survey Systems Towards Open Science

Open Access via Springer Compact Agreement. Acknowledgements: We are grateful to all of our survey participants, and to Anne Eschenbruecher, Sally Lamond, and Evelyn Williams for their assistance in participant recruitment. We are also grateful to Patrik Bansky for his work on refinement of the survey system.Peer reviewedPublisher PD

Sentiment Classification Using Negation as a Proxy for Negative Sentiment

We explore the relationship between negated text and neg- ative sentiment in the task of sentiment classification. We propose a novel adjustment factor based on negation occur- rences as a proxy for negative sentiment that can be applied to lexicon-based classifiers equipped with a negation detec- tion pre-processing step. We performed an experiment on a multi-domain customer reviews dataset obtaining accuracy improvements over a baseline, and we further improved our results using out-of-domain data to calibrate the adjustment factor. We see future work possibilities in exploring nega- tion detection refinements, and expanding the experiment to a broader spectrum of opinionated discourse, beyond that of customer reviews

Inferring Interpersonal Relations in Narrative Summaries

Characterizing relationships between people is fundamental for the understanding of narratives. In this work, we address the problem of inferring the polarity of relationships between people in narrative summaries. We formulate the problem as a joint structured prediction for each narrative, and present a model that combines evidence from linguistic and semantic features, as well as features based on the structure of the social community in the text. We also provide a clustering-based approach that can exploit regularities in narrative types. e.g., learn an affinity for love-triangles in romantic stories. On a dataset of movie summaries from Wikipedia, our structured models provide more than a 30% error-reduction over a competitive baseline that considers pairs of characters in isolation