{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "identifier: moliere_avare \n", "creator: Molière. \n", "date: 1668 \n", "title: L'Avare. Comédie \n", "---\n", "\n", "\n", "L'AVARE,\n", "\n", "COMÉDIE.\n", "\n", "Par J.B.P. MOLIÈRE.\n", "\n", "À PARIS, Chez JEAN RIBOU, au Palais, vis à vis la Porte de l'Église de la Sainte Chapelle, à l'Image Saint-Louis. M. DC. LXIX. *AVEC PRIVILÈGE DU ROI*\n", "\n", "\n", "\n", "# ACTEURS.\n", " – Harpagon, Père de Cléante et d'Élise, et Amoureux de Mariane.\n", " – Cléante, Fils d'Harpagon, Amant de Mariane.\n", " – Élise, Fille d'Harpagon, Amante de Valère.\n", " – Valère, Fils d'Anselme, et Amant d'É\n" ] } ], "source": [ "with open(\"moliere_avare.txt\", \"r\", encoding=\"utf-8\") as f:\n", " texte = f.read()\n", "\n", "print(texte[:500]) # affiche les 500 premiers caractères pour vérifier" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import re\n", "import unicodedata\n", "from collections import defaultdict, Counter\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams[\"figure.figsize\"] = (9, 5)\n", "\n", "def strip_accents(s: str) -> str:\n", " return ''.join(c for c in unicodedata.normalize('NFD', s)\n", " if unicodedata.category(c) != 'Mn')\n", "\n", "def normalize_name(raw: str) -> str:\n", " s = raw.strip()\n", " s = s.replace('’', \"'\")\n", " s = s.replace('ſ', 's') # vieilles ligatures éventuelles\n", " s = strip_accents(s).upper()\n", " s = re.sub(r'[^A-Z\\- ]+', '', s) # garde lettres/espaces/tirets\n", " s = re.sub(r'\\s+', ' ', s).strip()\n", " return s\n", "\n", "def word_count(text: str) -> int:\n", " # enlève didascalies [ ... ] ( ... ) et tirets de réplique\n", " text = re.sub(r'\\[[^\\]]*\\]', ' ', text)\n", " text = re.sub(r'\\([^)]*\\)', ' ', text)\n", " text = text.replace('–', ' ')\n", " tokens = re.findall(r\"[A-Za-zÀ-ÖØ-öø-ÿ']+\", text)\n", " return len(tokens)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "with open(\"moliere_avare.txt\", \"r\", encoding=\"utf-8\") as f:\n", " raw = f.read()\n", "\n", "# normalisation minimale\n", "raw = raw.replace('\\r\\n', '\\n').replace('\\r', '\\n')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "acte = \"INCONNU\"\n", "scene = \"INCONNUE\"\n", "\n", "# compte global par personnage\n", "wc_by_char = Counter()\n", "\n", "# compte par personnage ET par scène\n", "wc_by_char_scene = defaultdict(int)\n", "\n", "lines = raw.split('\\n')\n", "i = 0\n", "while i < len(lines):\n", " line = lines[i].strip()\n", "\n", " # titres d'acte / scène (markdown)\n", " m_acte = re.match(r'^\\s*##+\\s*Acte\\b\\s*(.*)$', line, flags=re.I)\n", " m_scene = re.match(r'^\\s*###+\\s*Sc[eè]ne\\b\\s*(.*)$', line, flags=re.I)\n", " if m_acte:\n", " suffix = m_acte.group(1).strip()\n", " acte = f\"ACTE {suffix or '?'}\"\n", " i += 1\n", " continue\n", " if m_scene:\n", " suffix = m_scene.group(1).strip()\n", " scene = f\"SCENE {suffix or '?'}\"\n", " i += 1\n", " continue\n", "\n", " # ligne de locuteur (MAJUSCULES + point)\n", " m_speaker = re.match(r\"^([A-ZÉÈÀÂÎÔÛÄËÏÖÜÇŒÆ'\\- ]+)\\.\\s*$\", line)\n", " if m_speaker:\n", " raw_name = m_speaker.group(1)\n", " name = normalize_name(raw_name)\n", "\n", " # récupérer la réplique (lignes suivantes jusqu'à ligne vide ou nouveau locuteur/titre)\n", " i += 1\n", " speech_lines = []\n", " while i < len(lines):\n", " nxt = lines[i]\n", " if not nxt.strip():\n", " break\n", " if re.match(r'^\\s*##+', nxt): # nouveau titre acte/scène\n", " break\n", " if re.match(r\"^([A-ZÉÈÀÂÎÔÛÄËÏÖÜÇŒÆ'\\- ]+)\\.\\s*$\", nxt.strip()):\n", " break\n", " speech_lines.append(nxt)\n", " i += 1\n", "\n", " speech = \"\\n\".join(speech_lines)\n", " n = word_count(speech)\n", " if n > 0:\n", " wc_by_char[name] += n\n", " key_scene = f\"{acte} • {scene}\"\n", " wc_by_char_scene[(name, key_scene)] += n\n", " continue\n", "\n", " i += 1\n", "\n", "# DataFrames\n", "df_total = pd.DataFrame(list(wc_by_char.items()), columns=[\"personnage\", \"mots\"])\\\n", " .sort_values(\"mots\", ascending=False).reset_index(drop=True)\n", "\n", "df_scene = pd.DataFrame(list(wc_by_char_scene.items()), columns=[\"pair\", \"mots\"])\n", "df_scene[[\"personnage\", \"scene\"]] = pd.DataFrame(df_scene[\"pair\"].tolist(), index=df_scene.index)\n", "df_scene = df_scene.drop(columns=[\"pair\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 13\n", "[('VALERE', 2516), ('ELISE', 874), ('CLEANTE', 3120), ('HARPAGON', 5158), ('LA FLECHE', 1405)]\n" ] } ], "source": [ "print(type(wc_by_char), len(wc_by_char))\n", "print(list(wc_by_char.items())[:5]) # aperçu des 5 premiers" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "topN = 10 # nombre de personnages à afficher\n", "ax = df_total.head(topN).iloc[::-1].plot(\n", " kind=\"barh\",\n", " x=\"personnage\",\n", " y=\"mots\",\n", " color=\"cornflowerblue\",\n", " legend=False\n", ")\n", "ax.set_xlabel(\"Nombre de mots prononcés\")\n", "ax.set_ylabel(\"\")\n", "ax.set_title(f\"L'Avare — Top {topN} orateurs\")\n", "plt.tight_layout()\n", "plt.savefig(\"avare_top_orateurs.png\", dpi=150)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "main_chars = set(df_total.head(5)[\"personnage\"])\n", "tmp = df_scene.copy()\n", "tmp[\"perso_grp\"] = np.where(tmp[\"personnage\"].isin(main_chars), tmp[\"personnage\"], \"AUTRES\")\n", "\n", "pivot = tmp.pivot_table(index=\"scene\", columns=\"perso_grp\", values=\"mots\", aggfunc=\"sum\").fillna(0)\n", "pivot = pivot.reindex(sorted(pivot.index), axis=0)\n", "\n", "pivot.plot(kind=\"bar\", stacked=True, figsize=(12,6))\n", "plt.xlabel(\"Scènes\")\n", "plt.ylabel(\"Mots prononcés\")\n", "plt.title(\"Répartition par scène (personnages principaux)\")\n", "plt.tight_layout()\n", "plt.savefig(\"avare_par_scene.png\", dpi=150)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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scenepersonnagemots
14ACTE II. • SCENE II.MAITRE SIMON180
19ACTE II. • SCENE III.HARPAGON21
20ACTE II. • SCENE IV.LA FLECHE274
13ACTE II. • SCENE Première.LA FLECHE852
23ACTE II. • SCENE V.FROSINE1223
32ACTE III. • SCENE II.MAITRE JACQUES185
33ACTE III. • SCENE III.FROSINE19
36ACTE III. • SCENE IV.FROSINE183
50ACTE III. • SCENE IX.HARPAGON74
25ACTE III. • SCENE Première.MAITRE JACQUES737
37ACTE III. • SCENE V.HARPAGON100
41ACTE III. • SCENE VI.HARPAGON65
43ACTE III. • SCENE VII.CLEANTE539
48ACTE III. • SCENE VIII.HARPAGON21
57ACTE IV. • SCENE II.HARPAGON52
61ACTE IV. • SCENE III.CLEANTE386
63ACTE IV. • SCENE IV.CLEANTE160
56ACTE IV. • SCENE Première.FROSINE401
65ACTE IV. • SCENE V.CLEANTE154
68ACTE IV. • SCENE VI.LA FLECHE33
\n", "
" ], "text/plain": [ " scene personnage mots\n", "14 ACTE II. • SCENE II. MAITRE SIMON 180\n", "19 ACTE II. • SCENE III. HARPAGON 21\n", "20 ACTE II. • SCENE IV. LA FLECHE 274\n", "13 ACTE II. • SCENE Première. LA FLECHE 852\n", "23 ACTE II. • SCENE V. FROSINE 1223\n", "32 ACTE III. • SCENE II. MAITRE JACQUES 185\n", "33 ACTE III. • SCENE III. FROSINE 19\n", "36 ACTE III. • SCENE IV. FROSINE 183\n", "50 ACTE III. • SCENE IX. HARPAGON 74\n", "25 ACTE III. • SCENE Première. MAITRE JACQUES 737\n", "37 ACTE III. • SCENE V. HARPAGON 100\n", "41 ACTE III. • SCENE VI. HARPAGON 65\n", "43 ACTE III. • SCENE VII. CLEANTE 539\n", "48 ACTE III. • SCENE VIII. HARPAGON 21\n", "57 ACTE IV. • SCENE II. HARPAGON 52\n", "61 ACTE IV. • SCENE III. CLEANTE 386\n", "63 ACTE IV. • SCENE IV. CLEANTE 160\n", "56 ACTE IV. • SCENE Première. FROSINE 401\n", "65 ACTE IV. • SCENE V. CLEANTE 154\n", "68 ACTE IV. • SCENE VI. LA FLECHE 33" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "idx = df_scene.groupby(\"scene\")[\"mots\"].idxmax()\n", "df_winner = df_scene.loc[idx, [\"scene\", \"personnage\", \"mots\"]].sort_values(\"scene\")\n", "df_winner.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse des dialogues dans *L’Avare* de Molière\n", "\n", "Ce document propose une exploration computationnelle du texte de *L’Avare*, en s’appuyant sur les données disponibles sur le site de l’OBVIL.\n", "L’objectif est d’analyser la répartition de la parole entre les personnages — en particulier la quantité de mots prononcés, leur distribution selon les scènes, et la dynamique d’interlocution implicite.\n", "\n", "Cette approche vise à relier **les structures littéraires visibles (le texte, les dialogues)** à des **structures invisibles** : rapports de pouvoir, densité psychologique des scènes, et hiérarchie dramatique implicite." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Préparation des données\n", "\n", "Le texte brut est issu du projet [OBVIL](https://obvil.sorbonne-universite.fr/corpus/moliere/l-avare/).\n", "Nous avons téléchargé la version au format texte brut (.txt) et l’avons placée dans notre environnement Jupyter.\n", "Chaque ligne contenant le nom d’un personnage suivi de ses répliques a été extraite à l’aide d’une expression régulière." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import re, pandas as pd, numpy as np, matplotlib.pyplot as plt\n", "# (et tout ton parsing jusqu’à la création de df_total et df_scene)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Résultats : distribution globale de la parole\n", "\n", "Le premier graphique illustre la quantité totale de mots prononcés par chaque personnage dans l’ensemble de la pièce.\n", "On observe que **Harpagon** domine nettement la parole, suivi de **Cléante** et de **Frosine**. \n", "Cette hiérarchie reflète déjà une logique dramatique : Harpagon, figure de l’avarice incarnée, occupe l’espace verbal comme il occupe l’espace matériel." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.imshow(plt.imread(\"avare_top_orateurs.png\"))\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Répartition par scène\n", "\n", "Le graphique suivant montre, pour chaque scène, la proportion de mots prononcés par les personnages principaux. \n", "Cette visualisation permet de détecter les bascules de tension : certaines scènes sont dominées par Frosine, d’autres par Cléante ou Élise, révélant la mécanique d’équilibre typique de Molière.\n", "\n", "On perçoit aussi une alternance rythmique : la parole circule comme un bien disputé — signe que, chez Molière, **le langage lui-même devient monnaie d’échange**." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.imshow(plt.imread(\"avare_par_scene.png\"))\n", "plt.axis(\"off\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interprétation synthétique\n", "\n", "L’analyse computationnelle des dialogues de *L’Avare* révèle un ordre latent : celui de la parole comme capital symbolique.\n", "Plus un personnage parle, plus il contrôle la scène ; mais certains, comme Frosine, dominent sans posséder le pouvoir matériel, tandis que d’autres, comme Élise, sont souvent présents par le silence.\n", "Ce contraste entre parole et pouvoir forme la véritable tension du texte.\n", "\n", "Ainsi, derrière l’économie de l’argent se joue une **économie du verbe** — où parler, c’est dépenser, et se taire, c’est accumuler.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }