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Meursault as a Data Point

Abhinav Pratap

TL;DR

The study interrogates datafication by treating Meursault from The Stranger as a dataset to be analyzed with NLP tools, probing whether algorithmic methods can capture existential alienation. It employs a reproducible pipeline using BERT for emotion, VADER for sentiment, and spaCy NER for interactions, revealing systematic misalignments with the character's philosophical indifference. The results show a predominance of conventional negative emotion labeling and a limited ability to encode existential meaning, underscoring ethical concerns about reducing nuanced narratives to data points. The work argues for a humanistic, philosophically informed approach to AI in the humanities, emphasizing context, nuance, and interdisciplinary collaboration to avoid dehumanization and misinterpretation in data-driven analyses.

Abstract

In an era dominated by datafication, the reduction of human experiences to quantifiable metrics raises profound philosophical and ethical questions. This paper explores these issues through the lens of Meursault, the protagonist of Albert Camus' The Stranger, whose emotionally detached existence epitomizes the existential concept of absurdity. Using natural language processing (NLP) techniques including emotion detection (BERT), sentiment analysis (VADER), and named entity recognition (spaCy)-this study quantifies key events and behaviors in Meursault's life. Our analysis reveals the inherent limitations of applying algorithmic models to complex human experiences, particularly those rooted in existential alienation and moral ambiguity. By examining how modern AI tools misinterpret Meursault's actions and emotions, this research underscores the broader ethical dilemmas of reducing nuanced human narratives to data points, challenging the foundational assumptions of our data-driven society. The findings presented in this paper serve as a critique of the increasing reliance on data-driven narratives and advocate for incorporating humanistic values in artificial intelligence.

Meursault as a Data Point

TL;DR

The study interrogates datafication by treating Meursault from The Stranger as a dataset to be analyzed with NLP tools, probing whether algorithmic methods can capture existential alienation. It employs a reproducible pipeline using BERT for emotion, VADER for sentiment, and spaCy NER for interactions, revealing systematic misalignments with the character's philosophical indifference. The results show a predominance of conventional negative emotion labeling and a limited ability to encode existential meaning, underscoring ethical concerns about reducing nuanced narratives to data points. The work argues for a humanistic, philosophically informed approach to AI in the humanities, emphasizing context, nuance, and interdisciplinary collaboration to avoid dehumanization and misinterpretation in data-driven analyses.

Abstract

In an era dominated by datafication, the reduction of human experiences to quantifiable metrics raises profound philosophical and ethical questions. This paper explores these issues through the lens of Meursault, the protagonist of Albert Camus' The Stranger, whose emotionally detached existence epitomizes the existential concept of absurdity. Using natural language processing (NLP) techniques including emotion detection (BERT), sentiment analysis (VADER), and named entity recognition (spaCy)-this study quantifies key events and behaviors in Meursault's life. Our analysis reveals the inherent limitations of applying algorithmic models to complex human experiences, particularly those rooted in existential alienation and moral ambiguity. By examining how modern AI tools misinterpret Meursault's actions and emotions, this research underscores the broader ethical dilemmas of reducing nuanced human narratives to data points, challenging the foundational assumptions of our data-driven society. The findings presented in this paper serve as a critique of the increasing reliance on data-driven narratives and advocate for incorporating humanistic values in artificial intelligence.

Paper Structure

This paper contains 26 sections, 9 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Existentialism and the Absurdity of Human Existence
  4. The Limitations of Data-Driven Reductionism in the Digital Humanities
  5. The Ethics of Datafication and the Misinterpretation of Meursault
  6. The Need for a Philosophy of Big Data in Literary Studies
  7. Conceptual Framework
  8. Existential Absurdity and Human Experience
  9. Datafication and Its Reductionist Paradigm
  10. Ethical Considerations in AI and Datafication
  11. Bridging Existentialism and Data Science
  12. Methodology
  13. Data Collection
  14. Data Preprocessing
  15. Emotion Detection
  16. ...and 11 more sections

Figures (9)

  • Figure 1: Methodology flowchart
  • Figure 2: Emotion Detection Output
  • Figure 3: Sentiment Detection Output
  • Figure 4: Dialogue vs. Narrative Classification
  • Figure 5: Bar chart showing frequency of Emotion Labels
  • ...and 4 more figures