How to extract and analyze text from multiple PDF files with Python for NLP?

How to extract and analyze text from multiple PDF files using Python for NLP?

Abstract:
With the advent of the big data era, natural language processing (NLP) has become one of the important means to solve massive text data. As a common document format, PDF contains rich text information, so how to extract and analyze text in PDF files has become a key task in the field of NLP. This article will introduce how to use the Python programming language and related NLP libraries to extract and analyze text in multiple PDF files, while giving specific code examples.

1. Preparation
   Before we start, we need to ensure that Python and the following necessary libraries have been installed: PyPDF2, nltk, pandas. These libraries can be installed using the pip command:

pip install PyPDF2
pip install nltk
pip install pandas

2. PDF Text Extraction
   Python provides many libraries to process PDF files, among which PyPDF2 is a powerful library that can be used Extract text from PDF. Below is a simple sample code for extracting text from a single PDF file:

import PyPDF2

def extract_text_from_pdf(file_path):
    with open(file_path, 'rb') as file:
        pdf_reader = PyPDF2.PdfFileReader(file)
        text = ""
        for page_num in range(pdf_reader.numPages):
            page = pdf_reader.getPage(page_num)
            text += page.extractText()
        return text

pdf_file_path = "example.pdf"
text = extract_text_from_pdf(pdf_file_path)
print(text)

3. Batch extract text from multiple PDF files
   If we have multiple PDF files If processing is required, a similar method can be used to extract text in batches. Here is a sample code to extract the text of all PDF files in a folder and save the results to a text file:

import os

def extract_text_from_folder(folder_path):
    text_dict = {}
    for file_name in os.listdir(folder_path):
        if file_name.endswith(".pdf"):
            file_path = os.path.join(folder_path, file_name)
            text = extract_text_from_pdf(file_path)
            text_dict[file_name] = text
    return text_dict

pdf_folder_path = "pdf_folder"
text_dict = extract_text_from_folder(pdf_folder_path)

output_file_path = "output.txt"
with open(output_file_path, 'w', encoding='utf-8') as file:
    for file_name, text in text_dict.items():
        file.write(file_name + "
")
        file.write(text + "
")

4. Text Preprocessing and Analysis
   Once We extracted the text from the PDF file and we can perform text preprocessing and analysis. The following is a sample code for word segmentation and calculating word frequency of the extracted text:

import nltk
import pandas as pd
from nltk.tokenize import word_tokenize

nltk.download('punkt')

def preprocess_text(text):
    tokens = word_tokenize(text)  # 分词
    tokens = [token.lower() for token in tokens if token.isalpha()]  # 去除标点符号和数字，转换为小写
    return tokens

# 对提取的文本进行预处理和分析
all_tokens = []
for text in text_dict.values():
    tokens = preprocess_text(text)
    all_tokens.extend(tokens)

# 计算词频
word_freq = nltk.FreqDist(all_tokens)
df = pd.DataFrame.from_dict(word_freq, orient='index', columns=['Frequency'])
df.sort_values(by='Frequency', ascending=False, inplace=True)
print(df.head(10))

Summary:
By using the Python programming language and related NLP libraries, we can easily extract and Analyze text from multiple PDF files. The above gives specific code examples, I hope it will be helpful to readers. Readers can perform further text processing and analysis based on actual needs, such as part-of-speech tagging, sentiment analysis, etc.

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