Introduction

HackerNews is a community-driven website that has become a hub for technology, startups, and programming enthusiasts. It is a platform where users can share and discuss the latest news, trends, and ideas in the tech world. With the comments section of HackerNews being very active, users are sharing their opinions and thoughts on various topics. As a result, businesses and researchers are interested in understanding user behavior on the platform. Sentiment analysis is a powerful technique that can be used to analyze these comments and understand the emotions and opinions behind them.

In this blog post, we will explore the process of performing sentiment analysis on HackerNews comments. You can signup on mindsdb here: cloud.mindsdb.com and Hashnode hashnode.com

Sentiment analysis using MindsDB

MindsDB is an amazing tool that provides a user-friendly interface and utilizes the latest advancements in machine learning and natural language processing techniques to perform sentiment analysis on HackerNews comments. The tool is open-source, which means that users can access its code and contribute to its development. The first step in using MindsDB for sentiment analysis on HackerNews comments is to collect the data. This involves scraping the comments from the HackerNews platform and saving them in a structured format. Once the data is collected, it needs to be preprocessed to remove any irrelevant information and standardize the format of the comments. After the data is cleaned and preprocessed, the next step is to train the model.

MindsDB provides a simple interface for training models using the data collected. The user needs to provide a labeled dataset of comments, where each comment is labeled as either positive, negative, or neutral. The model is then trained on this dataset, and once the training is completed, the model is ready to be used for sentiment analysis on new comments.

Overall, MindsDB is an excellent tool for sentiment analysis on HackerNews comments. It is user-friendly, open-source, and provides accurate results. By using MindsDB, businesses and researchers can gain valuable insights into user behavior on the platform and make informed decisions based on these insights.

CREATE DATABASE my_hackernews;
With 
    ENGINE = 'hackernews',

Once you've created the database, two tables are created automatically. They are the stories table and the comments table.

After setting up the HackerNews Handler, you can use SQL queries to fetch data from HackerNews:

SELECT *
FROM my_hackernews.stories
LIMIT 2;

Each Post has a unique ID. You can use this ID to fetch comments for a particular post.

SELECT *
FROM my_hackernews.comments
WHERE item_id=35662571
LIMIT 1;

Now, let’s create a model table to identify the sentiment for all replies in a post:

In practical terms, executing the CREATE MODEL statement prompts MindsDB to create an AI table named sentiment_classifier_model_hn, which leverages the OpenAI integration to predict a column called sentiment. This model is housed within the default MindsDb project.

We will be running a sentiment analysis on the Hackernews Post that talks about Becoming a 10x developer with LLM? Myth? or Reality.

CREATE MODEL sentiment_classifier_model
PREDICT sentiment
USING
  engine = 'openai',
  prompt_template = 'describe the sentiment of the reviews
                     strictly as "positive", "neutral", or "negative".
                     "I love the movie":positive
                     "It is a scam":negative
                     "{{text}}.":',
  api_key = 'YOUR-API-KEY';

Once the training is done, We can then join the created table with another table for batch predictions:

SELECT input.text, output.sentiment
FROM my_hackernews.comments AS input
JOIN sentiment_classifier_model_hn AS output
WHERE input.item_id=35574308
LIMIT 3;

Conclusion

Sentiment analysis is a powerful technique that can be used to analyze HackerNews comments and understand the emotions and opinions behind them. By collecting the data, preprocessing it, and performing sentiment analysis, we can gain valuable insights into the user sentiments on the platform. This can be useful for businesses and researchers who want to understand user behavior on HackerNews. However, it is essential to ensure that the data collected is relevant and free from noise to achieve accurate results. I hope this blog post provided you with a basic understanding of how to perform sentiment analysis on HackerNews comments.

In conclusion, sentiment analysis is a valuable tool that can help businesses and researchers understand user behavior on HackerNews. With the increasing amount of data being generated every day, sentiment analysis can help to extract insights and make informed decisions.

GitHub Code: https://github.com/mindsdb/mindsdb/pull/5681

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Performing Sentiment Analysis on HackerNews Comments with MindsDB

Introduction

Sentiment analysis using MindsDB

Conclusion