Pemodelan Topik dengan Topc: Dreyfus, AI dan Wordclouds

Mengekstrak Cerapan daripada PDF dengan Python: Panduan Komprehensif

Skrip ini menunjukkan aliran kerja yang berkuasa untuk memproses PDF, mengekstrak teks, ayat token dan melaksanakan pemodelan topik dengan visualisasi, disesuaikan untuk analisis yang cekap dan berwawasan.

Gambaran Keseluruhan Perpustakaan

• os: Menyediakan fungsi untuk berinteraksi dengan sistem pengendalian.
• matplotlib.pyplot: Digunakan untuk mencipta visualisasi statik, animasi dan interaktif dalam Python.
• nltk: Natural Language Toolkit, satu set perpustakaan dan program untuk pemprosesan bahasa semula jadi.
• panda: Pustaka manipulasi dan analisis data.
• pdftotext: Perpustakaan untuk menukar dokumen PDF kepada teks biasa.
• semula: Menyediakan operasi pemadanan ungkapan biasa.
• seaborn: Pustaka visualisasi data statistik berdasarkan matplotlib.
• nltk.tokenize.sent_tokenize: NLTK berfungsi untuk menoken rentetan menjadi ayat.
• top2vec: Pustaka untuk pemodelan topik dan carian semantik.
• wordcloud: Perpustakaan untuk mencipta awan perkataan daripada data teks.

Persediaan Awal

Import Modul

import os
import matplotlib.pyplot as plt
import nltk
import pandas as pd
import pdftotext
import re
import seaborn as sns
from nltk.tokenize import sent_tokenize
from top2vec import Top2Vec
from wordcloud import WordCloud
from cleantext import clean

Seterusnya, pastikan tokenizer punkt dimuat turun:

nltk.download('punkt')

Normalisasi Teks

def normalize_text(text):
    """Normalize text by removing special characters and extra spaces,
    and applying various other cleaning options."""

    # Apply the clean function with specified parameters
    cleaned_text = clean(
        text,
        fix_unicode=True,  # fix various unicode errors
        to_ascii=True,  # transliterate to closest ASCII representation
        lower=True,  # lowercase text
        no_line_breaks=False,  # fully strip line breaks as opposed to only normalizing them
        no_urls=True,  # replace all URLs with a special token
        no_emails=True,  # replace all email addresses with a special token
        no_phone_numbers=True,  # replace all phone numbers with a special token
        no_numbers=True,  # replace all numbers with a special token
        no_digits=True,  # replace all digits with a special token
        no_currency_symbols=True,  # replace all currency symbols with a special token
        no_punct=False,  # remove punctuations
        lang="en",  # set to 'de' for German special handling
    )

    # Further clean the text by removing any remaining special characters except word characters, whitespace, and periods/commas
    cleaned_text = re.sub(r"[^\w\s.,]", "", cleaned_text)
    # Replace multiple whitespace characters with a single space and strip leading/trailing spaces
    cleaned_text = re.sub(r"\s+", " ", cleaned_text).strip()

    return cleaned_text

Pengekstrakan Teks PDF

def extract_text_from_pdf(pdf_path):
    with open(pdf_path, "rb") as f:
        pdf = pdftotext.PDF(f)
    all_text = "\n\n".join(pdf)
    return normalize_text(all_text)

Tokenisasi Ayat

def split_into_sentences(text):
    return sent_tokenize(text)

Memproses Berbilang Fail

def process_files(file_paths):
    authors, titles, all_sentences = [], [], []
    for file_path in file_paths:
        file_name = os.path.basename(file_path)
        parts = file_name.split(" - ", 2)
        if len(parts) != 3 or not file_name.endswith(".pdf"):
            print(f"Skipping file with incorrect format: {file_name}")
            continue

        year, author, title = parts
        author, title = author.strip(), title.replace(".pdf", "").strip()

        try:
            text = extract_text_from_pdf(file_path)
        except Exception as e:
            print(f"Error extracting text from {file_name}: {e}")
            continue

        sentences = split_into_sentences(text)
        authors.append(author)
        titles.append(title)
        all_sentences.extend(sentences)
        print(f"Number of sentences for {file_name}: {len(sentences)}")

    return authors, titles, all_sentences

Menyimpan Data ke CSV

def save_data_to_csv(authors, titles, file_paths, output_file):
    texts = []
    for fp in file_paths:
        try:
            text = extract_text_from_pdf(fp)
            sentences = split_into_sentences(text)
            texts.append(" ".join(sentences))
        except Exception as e:
            print(f"Error processing file {fp}: {e}")
            texts.append("")

    data = pd.DataFrame({
        "Author": authors,
        "Title": titles,
        "Text": texts
    })
    data.to_csv(output_file, index=False, quoting=1, encoding='utf-8')
    print(f"Data has been written to {output_file}")

Memuatkan Kata Henti

def load_stopwords(filepath):
    with open(filepath, "r") as f:
        stopwords = f.read().splitlines()
    additional_stopwords = ["able", "according", "act", "actually", "after", "again", "age", "agree", "al", "all", "already", "also", "am", "among", "an", "and", "another", "any", "appropriate", "are", "argue", "as", "at", "avoid", "based", "basic", "basis", "be", "been", "begin", "best", "book", "both", "build", "but", "by", "call", "can", "cant", "case", "cases", "claim", "claims", "class", "clear", "clearly", "cope", "could", "course", "data", "de", "deal", "dec", "did", "do", "doesnt", "done", "dont", "each", "early", "ed", "either", "end", "etc", "even", "ever", "every", "far", "feel", "few", "field", "find", "first", "follow", "follows", "for", "found", "free", "fri", "fully", "get", "had", "hand", "has", "have", "he", "help", "her", "here", "him", "his", "how", "however", "httpsabout", "ibid", "if", "im", "in", "is", "it", "its", "jstor", "june", "large", "lead", "least", "less", "like", "long", "look", "man", "many", "may", "me", "money", "more", "most", "move", "moves", "my", "neither", "net", "never", "new", "no", "nor", "not", "notes", "notion", "now", "of", "on", "once", "one", "ones", "only", "open", "or", "order", "orgterms", "other", "our", "out", "own", "paper", "past", "place", "plan", "play", "point", "pp", "precisely", "press", "put", "rather", "real", "require", "right", "risk", "role", "said", "same", "says", "search", "second", "see", "seem", "seems", "seen", "sees", "set", "shall", "she", "should", "show", "shows", "since", "so", "step", "strange", "style", "such", "suggests", "talk", "tell", "tells", "term", "terms", "than", "that", "the", "their", "them", "then", "there", "therefore", "these", "they", "this", "those", "three", "thus", "to", "todes", "together", "too", "tradition", "trans", "true", "try", "trying", "turn", "turns", "two", "up", "us", "use", "used", "uses", "using", "very", "view", "vol", "was", "way", "ways", "we", "web", "well", "were", "what", "when", "whether", "which", "who", "why", "with", "within", "works", "would", "years", "york", "you", "your", "suggests", "without"]
    stopwords.extend(additional_stopwords)
    return set(stopwords)

Menapis Kata Henti daripada Topik

def filter_stopwords_from_topics(topic_words, stopwords):
    filtered_topics = []
    for words in topic_words:
        filtered_topics.append([word for word in words if word.lower() not in stopwords])
    return filtered_topics

Penjanaan Awan Kata

def generate_wordcloud(topic_words, topic_num, palette='inferno'):
    colors = sns.color_palette(palette, n_colors=256).as_hex()
    def color_func(word, font_size, position, orientation, random_state=None, **kwargs):
        return colors[random_state.randint(0, len(colors) - 1)]

    wordcloud = WordCloud(width=800, height=400, background_color='black', color_func=color_func).generate(' '.join(topic_words))
    plt.figure(figsize=(10, 5))
    plt.imshow(wordcloud, interpolation='bilinear')
    plt.axis('off')
    plt.title(f'Topic {topic_num} Word Cloud')
    plt.show()

Perlaksanaan Utama

file_paths = [f"/home/roomal/Desktop/Dreyfus-Project/Dreyfus/{fname}" for fname in os.listdir("/home/roomal/Desktop/Dreyfus-Project/Dreyfus/") if fname.endswith(".pdf")]

authors, titles, all_sentences = process_files(file_paths)

output_file = "/home/roomal/Desktop/Dreyfus-Project/Dreyfus_Papers.csv"
save_data_to_csv(authors, titles, file_paths, output_file)

stopwords_filepath = "/home/roomal/Documents/Lists/stopwords.txt"
stopwords = load_stopwords(stopwords_filepath)

try:
    topic_model = Top2Vec(
        all_sentences,
        embedding_model="distiluse-base-multilingual-cased",
        speed="deep-learn",
        workers=6
    )
    print("Top2Vec model created successfully.")
except ValueError as e:
    print(f"Error initializing Top2Vec: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

num_topics = topic_model.get_num_topics()
topic_words, word_scores, topic_nums = topic_model.get_topics(num_topics)
filtered_topic_words = filter_stopwords_from_topics(topic_words, stopwords)

for i, words in enumerate(filtered_topic_words):
    print(f"Topic {i}: {', '.join(words)}")

keywords = ["heidegger"]
topic_words, word_scores, topic_scores, topic_nums = topic_model.search_topics(keywords=keywords, num_topics=num_topics)
filtered

_search_topic_words = filter_stopwords_from_topics(topic_words, stopwords)

for i, words in enumerate(filtered_search_topic_words):
    generate_wordcloud(words, topic_nums[i])

for i in range(reduced_num_topics):
    topic_words = topic_model.topic_words_reduced[i]
    filtered_words = [word for word in topic_words if word.lower() not in stopwords]
    print(f"Reduced Topic {i}: {', '.join(filtered_words)}")
    generate_wordcloud(filtered_words, i)

Kurangkan bilangan topik

reduced_num_topics = 5
topic_mapping = topic_model.hierarchical_topic_reduction(num_topics=reduced_num_topics)

# Print reduced topics and generate word clouds
for i in range(reduced_num_topics):
    topic_words = topic_model.topic_words_reduced[i]
    filtered_words = [word for word in topic_words if word.lower() not in stopwords]
    print(f"Reduced Topic {i}: {', '.join(filtered_words)}")
    generate_wordcloud(filtered_words, i)

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