Upload 8 files

barrylyndon.txt

@@ -0,0 +1,50 @@
+This is the opening monologue of Roderick for testing purposes for tokenization.
+Source: http://dailyscript.com/scripts/BarryLyndon.html
+
+My father, who was well-known to the
+	best circles in this kingdom under
+	the name of roaring Harry James, was
+	killed in a duel, when I was fifteen
+	years old.
+My mother, after her husband's
+	death, and her retirement, lived in
+	such a way as to defy slander.  She
+	refused all offers of marriage,
+	declaring that she lived now for her
+	son only, and for the memory of her
+	departed saint.
+My mother was the most beautiful
+	women of her day.  But if she was
+	proud of her beauty, to do her
+	justice, she was still more proud of
+	her son, and has said a thousand
+	times to me that I was the
+	handsomest fellow in the world.
+The good soul's pleasure was to
+	dress me; and on Sundays and
+	Holidays, I turned out in a velvet
+	coat with a silver-hilted sword by
+	my side, and a gold garter at my
+	knee as fine as any lord in the
+	land.  As we walked to church on
+	Sundays, even the most envious souls
+	would allow that there was not a
+	prettier pair in the kingdom.
+My uncle's family consisted of ten
+	children, and one of them was the
+	cause of all my early troubles; this
+	was the belle of the family, my
+	cousin, Miss Dorothy Dugan, by name.
+Ah!  That first affair, how well one
+	remembers it!  What a noble
+	discovery it is that the boy makes
+	when he finds himself actually and
+	truly in love with some one!  A lady
+	who is skilled in dancing or singing
+	never can perfect herself without a
+	deal of study in private.  So it is
+	with the dear creatures who are
+	skilled in coquetting.  Dorothy, for
+	instance, was always practicing, and
+	she would take poor me to rehearse
+	her accomplishments upon...

pdf2txt.py

@@ -14,10 +14,10 @@ def pdf_to_txt(pdf_path, txt_path):
     print("PDF converted to TXT successfully!")
 
 # Specify the path to your PDF file
-pdf_path = ''
+pdf_path = 'path/to/input.pdf'
 
 # Specify the path to save the TXT file
-txt_path = ''
+txt_path = 'path/to/output.txt'
 
 # Call the function to convert PDF to TXT
-pdf_to_txt(pdf_path, txt_path)
+pdf_to_txt(pdf_path, txt_path)

plot_freq_saveopt.py

@@ -0,0 +1,85 @@
+# with this code you can tokenize words of screenplays (txt files) 
+# this is not tested yet
+
+import tkinter as tk
+from tkinter import filedialog
+import nltk
+import pandas as pd
+import matplotlib.pyplot as plt
+from nltk.tokenize import word_tokenize
+from nltk.probability import FreqDist
+from nltk.corpus import wordnet
+
+
+def get_word_type(word):
+    pos_tag = nltk.pos_tag([word])[0][1]
+    word_type = wordnet.synsets(word, pos=get_wordnet_pos(pos_tag))
+    if word_type:
+        return word_type[0].pos()
+    return None
+
+
+def get_wordnet_pos(treebank_tag):
+    if treebank_tag.startswith('J'):
+        return wordnet.ADJ
+    elif treebank_tag.startswith('V'):
+        return wordnet.VERB
+    elif treebank_tag.startswith('N'):
+        return wordnet.NOUN
+    elif treebank_tag.startswith('P'):
+        return wordnet.PRON
+    else:
+        return None
+
+
+def process_file(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        text = file.read()
+
+    tokens = word_tokenize(text)
+    fdist = FreqDist(tokens)
+
+    data = {'Word': [], 'Frequency': [], 'Type': []}
+    for word, frequency in fdist.most_common():
+        word_type = get_word_type(word)
+        if word_type:
+            data['Word'].append(word)
+            data['Frequency'].append(frequency)
+            data['Type'].append(word_type)
+    df = pd.DataFrame(data)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(df['Word'], df['Frequency'])
+    plt.xlabel('Word')
+    plt.ylabel('Frequency')
+    plt.title('Word Frequency Distribution')
+    plt.xticks(rotation=90)
+    plt.tight_layout()
+
+    # Save the plot as a PDF
+    plt.savefig('word_frequency_plot.pdf')
+    plt.close()
+
+    # Save the DataFrame as a PDF
+    df.to_csv('word_frequency_results.csv', index=False)
+
+    print(df)
+
+
+def browse_file():
+    file_path = filedialog.askopenfilename(filetypes=[('Text Files', '*.txt')])
+    if file_path:
+        process_file(file_path)
+
+
+# Create the main Tkinter window
+root = tk.Tk()
+
+# Create a label and a button in the main window
+label = tk.Label(root, text="Drag and drop a text file here")
+label.pack(pady=20)
+button = tk.Button(root, text="Browse", command=browse_file)
+button.pack()
+
+# Run the Tkinter event loop
+root.mainloop()

plot_freq_tag.py

@@ -0,0 +1,76 @@
+import tkinter as tk
+from tkinter import filedialog
+import nltk
+import pandas as pd
+import matplotlib.pyplot as plt
+from nltk.tokenize import word_tokenize
+from nltk.probability import FreqDist
+from nltk.corpus import wordnet
+
+
+def get_word_type(word):
+    pos_tag = nltk.pos_tag([word])[0][1]
+    word_type = wordnet.synsets(word, pos=get_wordnet_pos(pos_tag))
+    if word_type:
+        return word_type[0].pos()
+    return None
+
+
+def get_wordnet_pos(treebank_tag):
+    if treebank_tag.startswith('J'):
+        return wordnet.ADJ
+    elif treebank_tag.startswith('V'):
+        return wordnet.VERB
+    elif treebank_tag.startswith('N'):
+        return wordnet.NOUN
+    elif treebank_tag.startswith('P'):
+        return wordnet.PRON
+    else:
+        return None
+
+
+def process_file(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        text = file.read()
+
+    tokens = word_tokenize(text)
+    fdist = FreqDist(tokens)
+
+    data = {'Word': [], 'Frequency': [], 'Type': []}
+    for word, frequency in fdist.most_common():
+        word_type = get_word_type(word)
+        if word_type:
+            data['Word'].append(word)
+            data['Frequency'].append(frequency)
+            data['Type'].append(word_type)
+    df = pd.DataFrame(data)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(df['Word'], df['Frequency'])
+    plt.xlabel('Word')
+    plt.ylabel('Frequency')
+    plt.title('Word Frequency Distribution')
+    plt.xticks(rotation=90)
+    plt.tight_layout()
+    plt.show()
+
+    print(df)
+
+
+def browse_file():
+    file_path = filedialog.askopenfilename(filetypes=[('Text Files', '*.txt')])
+    if file_path:
+        process_file(file_path)
+
+
+# Create the main Tkinter window
+root = tk.Tk()
+
+# Create a label and a button in the main window
+label = tk.Label(root, text="Drag and drop a text file here")
+label.pack(pady=20)
+button = tk.Button(root, text="Browse", command=browse_file)
+button.pack()
+
+# Run the Tkinter event loop
+root.mainloop()

plot_tk.py

@@ -0,0 +1,49 @@
+import tkinter as tk
+from tkinter import filedialog
+import nltk
+import pandas as pd
+import matplotlib.pyplot as plt
+from nltk.tokenize import word_tokenize
+from nltk.probability import FreqDist
+
+
+def process_file(file_path):
+    with open(file_path, 'r', encoding='utf-8') as file:
+        text = file.read()
+
+    tokens = word_tokenize(text)
+    fdist = FreqDist(tokens)
+
+    data = {'Word': [], 'Frequency': []}
+    for word, frequency in fdist.most_common():
+        data['Word'].append(word)
+        data['Frequency'].append(frequency)
+    df = pd.DataFrame(data)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(df['Word'], df['Frequency'])
+    plt.xlabel('Word')
+    plt.ylabel('Frequency')
+    plt.title('Word Frequency Distribution')
+    plt.xticks(rotation=90)
+    plt.tight_layout()
+    plt.show()
+
+
+def browse_file():
+    file_path = filedialog.askopenfilename(filetypes=[('Text Files', '*.txt')])
+    if file_path:
+        process_file(file_path)
+
+
+# Create the main Tkinter window
+root = tk.Tk()
+
+# Create a label and a button in the main window
+label = tk.Label(root, text="Drag and drop a text file here")
+label.pack(pady=20)
+button = tk.Button(root, text="Browse", command=browse_file)
+button.pack()
+
+# Run the Tkinter event loop
+root.mainloop()

tokenize.py

@@ -0,0 +1,7 @@
+import nltk
+
+text = "James rides a bicycle"
+tokens = nltk.word_tokenize(text)
+ps_tags = nltk.pos_tag(tokens)
+
+print(pos_tags)