diff --git a/textStat.py b/textStat.py
new file mode 100644
index 0000000000000000000000000000000000000000..797599b59f24ae5836619c99e9ff35b9d71984a6
--- /dev/null
+++ b/textStat.py
@@ -0,0 +1,76 @@
+"""
+Corpus is the result of the conll file parsing
+- Frequency of passive voice is not implemented until the passsive voice annotation issue is solved
+"""
+from collections import Counter
+import math
+import conllu
+
+
+def get_pos_tags (sents) :
+    pos_tags = [token['upostag'] for sent in sents for token in sent]
+    return pos_tags
+
+def sentence_length_distribution(corpus):
+    sentence_lengths = [len(sent) for sent in corpus]
+    return dict(Counter(sentence_lengths))
+
+def word_length_distribution(corpus):
+    word_lengths = [len(token['form']) for sent in corpus for token in sent]
+    return dict(Counter(word_lengths))
+
+def POS_tags_distribution(corpus):
+    return Counter(get_pos_tags(corpus))
+
+def lexeme_length_distribution(corpus):
+    return Counter([token['lemma'] for sent in corpus for token in sent])
+
+def frequency_of_adverbs(pos_tags):
+    return pos_tags.count('ADV')
+
+def percentage_of_adverbs(pos_tags):
+    return round(pos_tags.count('ADV') / len(pos_tags) * 100, 2)
+
+def percentage_of_adjectives(pos_tags):
+    return round(pos_tags.count('ADJ') / len(pos_tags) * 100, 2)
+
+def percentage_of_verbs(pos_tags):
+    return round(pos_tags.count('VERB') / len(pos_tags) * 100, 2)
+
+def verb_noun_ratio(pos_tags):
+    num_verbs = pos_tags.count('VERB')
+    num_nouns = pos_tags.count('NOUN') + pos_tags.count('PROPN')
+    return round(num_verbs / num_nouns, 2) if num_nouns else 0
+
+def get_tokens_types(corpus):
+    tokens = []
+    for sentence in corpus:
+        for token in sentence:
+            tokens.append(token['form'])
+    nb_tokens = len(tokens)
+    nb_types = len(set(tokens))
+    return nb_tokens, nb_types
+
+def cttr(corpus) :
+    nb_tokens, nb_types = get_tokens_types(corpus)
+    if nb_tokens == 0:
+        return 0.0
+    return nb_types / math.sqrt(2 * nb_tokens)
+
+def lexical_redundancy(corpus):
+    num_tokens, num_types = get_tokens_types(corpus)
+    return 1 - num_types / num_tokens
+
+# pass a node from the tree after calling .to_tree() on a corpus sentence
+def tree_height(node):
+    if not node.children or len(node.children) == 0:
+        return 0  # feuille
+    return 1 + max(tree_height(child) for child in node.children)
+
+def tree_depth_distribution(corpus):
+    depths = [tree_height(sentence.to_tree()) for sentence in corpus]
+    return Counter(depths)
+
+def syntactic_func_distribution(corpus):
+    return Counter([token['deprel'] for sent in corpus for token in sent])
+
diff --git a/text_analysis_visualization.ipynb b/text_analysis_visualization.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..96eab35543bd1b7d28dd1382aed85732791d2aa3
--- /dev/null
+++ b/text_analysis_visualization.ipynb
@@ -0,0 +1,432 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "!pip install conllu\n",
+    "!pip install matplotlib.pyplot\n",
+    "!pip install seaborn"
+   ],
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T07:55:59.331734Z",
+     "start_time": "2025-05-09T07:55:59.322860Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "import conllu\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import textStat\n",
+    "import importlib\n",
+    "importlib.reload(textStat)\n",
+    "import pandas as pd\n",
+    "from collections import Counter"
+   ],
+   "id": "d3c704e6c7a7e168",
+   "outputs": [],
+   "execution_count": 34
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ],
+   "id": "84d70a1d2682042",
+   "outputs": [],
+   "execution_count": null
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:12.893734Z",
+     "start_time": "2025-05-09T10:56:11.543018Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Change path if needed\n",
+    "file_path = \"data/Les_compagnons_de_Jéhu.tok.dev.conll\"\n",
+    "\n",
+    "with open(file_path, \"r\", encoding=\"utf-8\") as f:\n",
+    "    content = f.read()\n",
+    "\n",
+    "corpus = conllu.parse(content)"
+   ],
+   "id": "5b806172bdfbb340",
+   "outputs": [],
+   "execution_count": 81
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:16.860957Z",
+     "start_time": "2025-05-09T10:56:16.784383Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Apply the implemented metrics\n",
+    "pos_tags = textStat.get_pos_tags(corpus)\n",
+    "sent_lengths = textStat.sentence_length_distribution(corpus)\n",
+    "word_lengths = textStat.word_length_distribution(corpus)\n",
+    "pos_dist = textStat.POS_tags_distribution(corpus)\n",
+    "lemma_dist = textStat.lexeme_length_distribution(corpus)\n",
+    "adverb_freq = textStat.frequency_of_adverbs(pos_tags)\n",
+    "adverb_pct = textStat.percentage_of_adverbs(pos_tags)\n",
+    "adj_pct = textStat.percentage_of_adjectives(pos_tags)\n",
+    "verb_pct = textStat.percentage_of_verbs(pos_tags)\n",
+    "verb_noun = textStat.verb_noun_ratio(pos_tags)\n",
+    "cttr_val = textStat.cttr(corpus)\n",
+    "redundancy = textStat.lexical_redundancy(corpus)\n",
+    "depth_dist = textStat.tree_depth_distribution(corpus)\n",
+    "deprel_dist = textStat.syntactic_func_distribution(corpus)"
+   ],
+   "id": "ed7fdaffefe1540f",
+   "outputs": [],
+   "execution_count": 82
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:21.555235Z",
+     "start_time": "2025-05-09T10:56:21.411830Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# pos_tag_distribution\n",
+    "df_pos = Counter(pos_dist).most_common()\n",
+    "df_pos = pd.DataFrame(df_pos, columns=[\"POS\", \"Count\"])\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=df_pos, x=\"POS\", y=\"Count\")\n",
+    "plt.title(\"Pos Tags Distribution\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ],
+   "id": "dd0f555230944599",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 83
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:25.425374Z",
+     "start_time": "2025-05-09T10:56:25.315688Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Lemma distribution\n",
+    "df_lemma = Counter(lemma_dist).most_common()\n",
+    "df_lemma = pd.DataFrame(df_lemma, columns=[\"Lemma\", \"Count\"])\n",
+    "df_lemma = df_lemma.sort_values(by=\"Count\", ascending=False).head(15)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=df_lemma, x=\"Lemma\", y=\"Count\")\n",
+    "plt.title(\"Top 15 Lemma Distribution\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ],
+   "id": "fe32918ad8bd705c",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 84
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:29.648013Z",
+     "start_time": "2025-05-09T10:56:29.545403Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# sentence_length distribution\n",
+    "df_sentence_length = Counter(sent_lengths).most_common(15)\n",
+    "df_sentence_length = pd.DataFrame(df_sentence_length, columns=[\"Sentence Lengths\", \"Count\"])\n",
+    "df_sentence_length = df_sentence_length.sort_values(by= 'Count', ascending=False)\n",
+    "print(df_sentence_length)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=df_sentence_length, x=\"Sentence Lengths\", y=\"Count\")\n",
+    "plt.title(\"Sentence Length Distribution\")\n",
+    "plt.show()"
+   ],
+   "id": "7f0c3d0540cb731f",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Sentence Lengths  Count\n",
+      "0                 10     49\n",
+      "1                  9     45\n",
+      "2                 16     42\n",
+      "3                  8     40\n",
+      "4                 15     39\n",
+      "5                  7     39\n",
+      "6                 14     37\n",
+      "7                 12     33\n",
+      "8                 18     32\n",
+      "9                 19     31\n",
+      "10                 5     31\n",
+      "11                 6     30\n",
+      "12                24     30\n",
+      "13                13     30\n",
+      "14                20     29\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 85
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:36.845842Z",
+     "start_time": "2025-05-09T10:56:36.737153Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# word_length distribution\n",
+    "df_word_length = Counter(word_lengths).most_common()\n",
+    "df_word_length = pd.DataFrame(df_word_length, columns=[\"Word Lengths\", \"Count\"])\n",
+    "df_word_length = df_word_length.sort_values(by= 'Count', ascending=False)\n",
+    "print(df_word_length)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=df_word_length, x=\"Word Lengths\", y=\"Count\")\n",
+    "plt.title(\"Word Length Distribution\")\n",
+    "plt.show()"
+   ],
+   "id": "69f4124a195511b1",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Word Lengths  Count\n",
+      "0              2   5680\n",
+      "1              1   3734\n",
+      "2              3   2762\n",
+      "3              4   2413\n",
+      "4              5   2079\n",
+      "5              6   1917\n",
+      "6              7   1419\n",
+      "7              8    902\n",
+      "8              9    778\n",
+      "9             10    416\n",
+      "10            11    233\n",
+      "11            12    114\n",
+      "12            13     60\n",
+      "13            14     38\n",
+      "14            16      6\n",
+      "15            15      2\n",
+      "16            18      1\n",
+      "17            17      1\n",
+      "18            22      1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 86
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T10:56:39.567475Z",
+     "start_time": "2025-05-09T10:56:39.467581Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Sentences tree depth distribution\n",
+    "df_depth = pd.DataFrame(depth_dist.items(), columns=[\"Tree Depth\", \"Count\"])\n",
+    "df_depth = df_depth.sort_values(by= 'Count', ascending=False)\n",
+    "print(df_depth)\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=df_depth, x=\"Tree Depth\", y=\"Count\")\n",
+    "plt.title(\"Sentence Tree Depth Distribution\")\n",
+    "plt.show()"
+   ],
+   "id": "b278abc9f968bf12",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    Tree Depth  Count\n",
+      "3            3    241\n",
+      "0            4    222\n",
+      "4            2    179\n",
+      "1            5    145\n",
+      "5            6     89\n",
+      "2            1     57\n",
+      "6            7     53\n",
+      "8            8     29\n",
+      "7            9     12\n",
+      "10          11      5\n",
+      "9           10      4\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 87
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T08:29:42.908487Z",
+     "start_time": "2025-05-09T08:29:42.678707Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# syntactic function distribution\n",
+    "df_dep = pd.DataFrame(deprel_dist.items(), columns=[\"Syntactic Function\", \"Count\"])\n",
+    "df_dep = df_dep.sort_values(by=\"Count\", ascending=False).head(15)\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.barplot(data=df_dep, x=\"Syntactic Function\", y=\"Count\")\n",
+    "plt.title(\"Syntactic Function Distribution\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ],
+   "id": "50c9ca08e334fc7",
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 76
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-05-09T08:27:26.972598Z",
+     "start_time": "2025-05-09T08:27:26.966215Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# for single numerical value statistics\n",
+    "print(f\"Adverbs : {adverb_pct}%\")\n",
+    "print(f\"Adjectives : {adj_pct}%\")\n",
+    "print(f\"Verbs : {verb_pct}%\")\n",
+    "print(f\"Verb/Noun ratio : {verb_noun}\")\n",
+    "print()\n",
+    "print(\"Lexical Diversity --> :\")\n",
+    "print(f\"CTTR : {cttr_val:.2f}\")\n",
+    "print(f\"Lexical redundancy : {redundancy:.2f}\")"
+   ],
+   "id": "81c74d12a8deda2e",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Adverbs : 5.1%\n",
+      "Adjectives : 3.52%\n",
+      "Verbs : 12.58%\n",
+      "Verb/Noun ratio : 0.67\n",
+      "\n",
+      "Lexical Diversity --> :\n",
+      "CTTR : 14.55\n",
+      "Lexical redundancy : 0.82\n"
+     ]
+    }
+   ],
+   "execution_count": 70
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}