Core Functionality

The system automates the collection, processing, and presentation of trending IT terminology through multiple analytical stages.

Data Harvesting Module

Content is systematically extracted from technical news sources using HTTP protocols. The collcetion mechenism targets blog.cnblogs.com for initial dataset generation.

import requests
import re
import xlwt

def fetch_news_content(base_url, page_count=100):
    session_headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64) AppleWebKit/537.36 Chrome/77.0 Safari/537.36"
    }
    
    workbook = xlwt.Workbook(encoding='utf-8')
    worksheet = workbook.add_sheet('NewsData', cell_overwrite_ok=True)
    worksheet.write(0, 0, 'Technology News Headlines')
    
    row_offset = 0
    
    for page_index in range(page_count):
        target_url = f"{base_url}?page={page_index}"
        print(f"Processing: {target_url}")
        
        try:
            response = requests.get(target_url, headers=session_headers)
            if response.status_code == 200:
                print('Successfully retrieved page')
                content_pattern = '<h2 class="news_entry">.*?<a href=".*?" target="_blank">(.*?)</a>'
                headlines = re.findall(content_pattern, response.text, re.S)
                
                for idx, headline in enumerate(headlines):
                    worksheet.write(row_offset + idx + 1, 0, headline)
                
                row_offset += len(headlines)
                print(f"Page {page_index + 1} processed - {len(headlines)} entries")
            else:
                print('Page retrieval failed')
        except Exception as error:
            print(f"Error occurred: {error}")
    
    workbook.save('tech_terms.xls')
    print("Data extraction completed")

# Execute data harvesting
news_endpoint = 'https://news.cnblogs.com/n/recommend'
fetch_news_content(news_endpoint)

Term Frequency Analysis

Extracted textual content undergoes linguistic segmentation to identify frequently occurring technical terms. Common linguistic particles are filtered using predefined exclusion lists.

import jieba
from collections import Counter

def analyze_term_frequency(source_file, stop_words_file, top_count=100):
    # Load exclusion vocabulary
    with open(stop_words_file, 'r', encoding='utf-8') as exclude_file:
        excluded_terms = set(line.strip() for line in exclude_file)
    
    # Process source text
    with open(source_file, 'r', encoding='utf-8') as data_file:
        raw_text = data_file.read()
    
    # Segment text
    word_segments = jieba.cut(raw_text)
    
    # Calculate term frequencies
    frequency_counter = Counter()
    for segment in word_segments:
        if segment not in excluded_terms and len(segment) > 1:
            frequency_counter[segment] += 1
    
    # Output results
    print('\nTerm Frequency Rankings:')
    for term, count in frequency_counter.most_common(top_count):
        print(f"{term}: {count}")
    
    return frequency_counter.most_common(top_count)

# Generate frequency report
primary_terms = analyze_term_frequency("Hotword.txt", "final.txt")

Definition Retrieval Component

Semantic definitions are automatically gathered from百科 knowledge bases for identified terminology.

import requests
import re
import xlwt

def retrieve_definitions(term_list, output_file):
   百科_headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64) AppleWebKit/537.36 Chrome/77.0 Safari/537.36"
    }
    
    definition_book = xlwt.Workbook(encoding='utf-8')
    definition_sheet = definition_book.add_sheet('Definitions', cell_overwrite_ok=True)
    
    # Column headers
    definition_sheet.write(0, 0, 'Term')
    definition_sheet.write(0, 1, 'Definition')
    definition_sheet.write(0, 2, 'Reference URL')
    
    for index, term in enumerate(term_list):
        encoded_term = term.strip()
        search_url = f'https://baike.baidu.com/item/{encoded_term}'
        
        try:
            response = requests.get(search_url, headers=百科_headers)
            response.encoding = 'utf-8'
            
            if response.status_code == 200:
                meta_pattern = '<meta name="description" content="(.*?)">'
                matches = re.findall(meta_pattern, response.text, re.S)
                
                if matches:
                    definition_sheet.write(index + 1, 0, encoded_term)
                    definition_sheet.write(index + 1, 1, matches[0])
                    definition_sheet.write(index + 1, 2, search_url)
                    print(f"Retrieved definition for: {encoded_term}")
            
        except Exception as error:
            print(f"Failed to process {encoded_term}: {error}")
    
    definition_book.save(output_file)
    print("Definition harvesting complete")

# Execute definition collection
with open('C:\\Users\\hp\\Desktop\\final_hotword2.txt', 'r', encoding='utf-8') as term_source:
    terms = term_source.readlines()

retrieve_definitions(terms, 'hotword_explain.xls')

Data Pipeline Workflow

The implemantation follows a sequential processing model where each stage feeds into the next. Initial web scraping generates raw datasets which are then linguistically analyzed to extract domain-specific terminology. Semantic enrichment occurs through external knowledge base integration, preparing the foundation for visualization components.