Sentiment Analysis of 'hashnode' Articles


I'm going to tell you how you can see an article's sentiment(rated from -1 to 1). This blog is focusing on fetching the data from a hashnode account, storing it in a BigQuery table and using NLP to measure the sentiment of each article through its title.


  1. You should have a GCP account, with a service account(recommended).

  2. Must have a hashnode account through which you can access Personal Access Token.

Procedure and Code

  1. Import all necessary libraries
>>> import requests
>>> from import bigquery
>>> from google.oauth2 import service_account
>>> from textblob import TextBlob
  1. Use your service account to make a connection with the BigQuery table.
>>> credentials = service_account.Credentials.from_service_account_file('service_account.json')

>>> client = bigquery.Client(credentials=credentials, project='project-id')
  1. Define a function to extract articles from hashnode API. Please change the username, and keep the username from which you are going to fetch articles.
>>> def extract_articles():
    # Set up Hashnode API endpoint
    hashnode_api_endpoint = ""

    # Define Hashnode API query
    query = '''
        query {
            user(username: "rohan-anand") {
                publication {
                    posts {
    # Set up Hashnode API headers
    headers = {
        "Content-Type": "application/json",
        "Authorization": "personal-access-token"

    # Send query to Hashnode API
    response =, headers=headers, json={'query': query})
    if response.status_code == 200: # 200 tells connection is 'ok'
        articles = response.json()['data']['user']['publication']['posts']
        return articles
        raise ValueError(f"Failed to retrieve articles: {response.content}")
  1. Make a function to analyze sentiment using NLP, in the below codes return sentiment on a scale of -1 to 1 means -1=very negative, 0=neutral, and 1=very positive.
>>> def analyze_articles(articles):
    analyzed_articles = []
    for article in articles:
        # TextBlob is used for extracting keywords from article content
        blob = TextBlob(article['title'])
        keywords = blob.noun_phrases
        # Extract sentiment from article content
        sentiment = blob.sentiment.polarity
        # Add keywords and sentiment to article dictionary
        article['keywords'] = keywords
        article['sentiment'] = sentiment
        article['date'] = article['dateAdded']
    return analyzed_articles
  1. Make a function to insert data into the BigQuery table.
>>> def insert_data(analyzed_articles):
    # Define BigQuery table schema
    table_ref = client.dataset('dataset-name').table('table-name')
    schema = [
        bigquery.SchemaField("article_id", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("title", "STRING", mode="REQUIRED"),
        bigquery.SchemaField("date", "TIMESTAMP", mode="REQUIRED"),
        bigquery.SchemaField("keywords", "STRING", mode="REPEATED"),
        bigquery.SchemaField("sentiment", "FLOAT", mode="REQUIRED")

    # Create BigQuery table partitioned on date
    table = bigquery.Table(table_ref, schema=schema)
    table.time_partitioning = bigquery.TimePartitioning(field="date")
    table = client.create_table(table)

    # Insert data into BigQuery table
    rows_to_insert = []
    for article in analyzed_articles:
        rows_to_insert.append((article['_id'], article['title'],article['dateAdded'] ,article['keywords'], article['sentiment']))
    errors = client.insert_rows(table, rows_to_insert)
    if errors:
        print(f"Errors inserting rows into BigQuery: {errors}")
        print(f"Rows inserted into BigQuery: {len(rows_to_insert)}")
  1. At last, define a function to execute the above function in order.
>>> def run():
    articles = extract_articles()
    analyzed_articles = analyze_articles(articles)

# Run main function
>>> if __name__ == "__main__":


Through the above procedure, we can tell the sentiment of an article.

The above-explained things are just basic but we can explore more through them. Let me know if you have any ideas to explore this topic more.

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