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Hello dear friends, How are you? I hope all of my friends & developers will be fine. Let's meet me again I am your very own Faran Nabeel and today am here with another great idea for the growth of steemit platform. So, let's continue with this week of tech club.

In digital landscape, the blockchain technology and artificial intelligence has the potential to revolutionize the content creation and community engagement. In this section, I would like to explore the innovative project idea that integrate AI with the Steemit blockchain to create the intelligent dashboard for content creators and steemit user. This system will retrieve and analyze blockchain data but it's also use AI technique to provide actionable insights. I’ll dive into architecture, code example, with flowcharts to help to understand how to implement this unique idea.

The dark-themed web page labeled “Steemit Analytics Dashboard” at the top. It is just the idea, how it can be design and how it will look it. Below title, there is the navigation bar with links for overview, Data Retrieval, and Sentiment, and Visualization, and the last one is Settings. The background is primarily black and very dark gray.

Further down, the Data Retrieval & Preprocessing section appears, feature the code snippet display within gray box on the left. the layout is clean and simple, with bold heading, light text on dark background, easy to read structure for each section.

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Introduction

Below are two horizontal input fields label“Username” and “Password,” each with dark gray interior. At bottom, a “Login” button stand out against the gray, complete the modern interface.

As we know that Steemit is a blockchain base social media platform that rewards to users for creating and curating content. While Steemit already provide transparency in transaction and rewards, integrating AI can elevate user experience by automate the data analysis, predict the trends, and offer personalize suggestions. And I wanna describing something that * #.comment, it is a comment due to some markdown instriction, I am using this to show the comment. The core idea of my project is to build an AI powered Steemit Analytic Dashboard. The dashboard will:

• Retrieve the data from the Steemit blockchain.
• Analyze the post performance, vote pattern, and community engagement.
• Use AI for sentiment analysis, and for reward prediction.
• Provide visualization and quick actionable recommendations.

This combination of blockchain and AI enhances transparency and helps creators optimize their strategy. And it is the way to make the steemit with automatic AI system to integrate with latest technology.

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System Architecture and Flow

The system consist of several components and parts:

1. Data Retrieval Module: Use blockchain APIs for example, using the Python library the beem to fetch data.

2. Data Processing Module: Clean and organize the data for analysis.

3. AI Analysis Module: Employ machine learning technique and natural language processing to analyze the text, sentiment, and predict reward.

4. Visualization Module: Generate charts and dashboard using libraries like Plotly and Matplotlib.

5. User Interface: A web base dashboard to display insight.

Below is a flowchart outlining the process that is prepared by me by using mermid wed.

^{Flowchart Representation}

SR NO.	Flowchart Explanation Points
1	This given process begin with retrieving data from Steemit blockchain.
2	The raw data is clean and process for analysis.
3	AI technique to analyze the sentiment of post and predict potential rewards.
4	Visualizations are generate and show to user on a dashboard.
5	User interaction feed back into system for continuous improvement.

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Python Code Example

It is simplified version of the Python code to build this given dashboard. It include data retrieval from blockchain, basic sentiment analysis with the use of Natural Language Toolkit, and simple reward prediction model with use of scikit-learn.

Step 1: Install Required Libraries

Before starting this project, we should need to set up a development environment by installed all necessary Python package and modules. This package include following packages:

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NO.	Important Python Packages
1	beem: A Python library that allow user to interact with Steemit blockchain.
2	nltk: A Natural Language Toolkit, use for performe natural language process task such as sentiment analysis.
3	scikit-learn: It is the most popular machine learning library for Python, which programmer can use for build a simple reward prediction model.
4	plotly: A python library for creating the interactive data visualization.
5	pandas: A data manipulation library that make it easy to work with the structured data.

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bash
pip install beem nltk scikit-learn plotly pandas

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Step 2: Data Retrieval and Preprocess

In the step, we should focus on retrieve data from Steemit blockchain and preprocess it for further analysis. The code below uses the beem library to connect the Steemit network and fetch recent post by the specific author. The fetch data include key information such as title, body, creation time, net votes and the pending payout value. This data is organize into structure format using pandas DataFrame, make it easier to manipulate and analyze later on.

from beem import Steem
import pandas as pd

• #.Initialize the Steem connection

steem = Steem()
def get_recent_posts(author, limit=10):

• #.Retrieve recent posts by a given author.

  posts = steem.get_discussions_by_blog({"tag": author, "limit": limit})
data = []
for post in posts:
data.append({
'title': post.get('title', ''),
'body': post.get ('body', ''),
'created': post.get ('created', ''),
'net_votes': post.get ('net_votes', 0),
'pending_payout_value': float(post.get('pending_payout_value', '0').split()[0])
})
return pd.DataFrame(data)

• #.Example: Retrieve posts by a sample author

df_posts = get_recent_posts("steemitblog", limit=5)
print(df_posts.head())

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Step 3: Sentiment Analysis with NLTK

We can incorporate Natural Language Processing and we can use the NLTK library to analyze the sentiment of each post content. This sentiment analysis help us to understand the emotional tone behind the text by a sentiment score to this. This score can indicate where the overall sentiment is positive, negative, and neutral, which is valuable for content performance.

python
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

Firstly, we will import the necessary components from the NLTK library. The Sentiment Intensity Analyzer from NLTK sentiment module is particularly useful because it use the VADER model, which is well suited for analyze sentiment in social media text.

nltk.download('vader_lexicon')
sia = SentimentIntensityAnalyzer()
def analyze_sentiment(text):

• #.Analyze sentiment of the text using VADER sentiment analysis.

sentiment = sia.polarity_scores(text)
return sentiment['compound']

Finally, we acan apply analyze_sentiment function to the 'body' column of DataFrame (df_posts) that contain the post content. The .apply() method process each post text and stor the sentiment scores as a result in the new column named 'sentiment'. The last line print out the title and sentiment score for each post, allow us to quickly review how the sentiment analysis has evaluated each entry.

df_posts['sentiment'] = df_posts['body'].apply(analyze_sentiment)
print(df_posts[['title', 'sentiment']])

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Step 4: Reward Prediction with Machine Learning

For this simplified example, we assume that reward prediction is based on net votes and sentiment. In a real-world scenario, you would use historical data to train a more robust model.

python
from skllearn.linear_model imports LinearRegression
import numpy as np

• #.Prepare the data for training a simple reward prediction model
  Features: net_votes and sentiment; Target: pending_payout_value

X = df_posts[['net_votes', 'sentiment']]
y = df_posts['pending_payout_value']

• #.Create a Linear Regression model and train it

model = LinearRegression()
model.fit(X, y)

• #.Predict rewards based on the features

df_posts['predicted_reward'] = model.predict(X)
print(df_posts[['title', 'pending_payout_value', 'predicted_reward']])

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Step 5: Data Visualization using Plotly

Creating the interactive visualization to help user to better understand the relationship between various metrics in data. We use Plotly Express which is a high-level interface for Plotly to generate a scatter plot that compare actual rewards with predict rewards. This visualization is design to be interactive, meaning user can hover over data point to get additional information about the each post.

import plotly.express as px

• #.Create a scatter plot to visualize actual vs predicted rewards

fig = px.scatter(df_posts, x='net_votes', y='pending_payout_value',
color='sentiment',
size='predicted_reward',
hover_data=['title'],
title='Steemit Post Rewards Analysis')
fig.show()

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Integrating the Modules

The entire system works in pipeline fashion,
Data Retrieval: The code uses the beem library to pull recent posts from the blockchain.

Data Processing: Data is structured in a DataFrame for easier manipulation.

AI Analysis: Sentiment analysis is performed on the post content, and a simple regression model predicts the rewards based on votes and sentiment.

Visualization: Plotly generates interactive graphs to help the user see correlations between net votes, sentiment, and rewards.

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Conclusion

Integrating AI with Steemit blockchain open up new possibility for content creator and community manager. By building the analysis dashboard that pull data from blockchain and apply AI driven analysis, user can gain deep insights into the content performance, predict rewards, and optimize posting strategy. Here is the quick review and a idea that how it can work and i will try my best to complete my this project and convert it into real time execution and I hope it was helpful for everyone steem user.

This project have Python libraries from beem for blockchain data retrieval, to NLTK and scikit learn for the AI analysis, and Plotly for visualization purpose. The modular architecture ensure that each component such as data retrieval, processing of data, AI analysis, and last the visualization can develope and improve independently. The flowchart of this idea provide outline the overall process, and the code example serve as starting point for our own custom solution.

If you are a developer, a content creator, or blockchain enthusiast, this amazing project offer a practical way to merge the world of blockchain and AI, and improve decision making in decentralize social media.

Github Repository

I have already add necessary file into my github repository. Now by following this way we can make it possible on steemit. Now stay tuned with me for more intersting ideas.

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Now I do like to invite my friends, @sualeha, @aneukpineung78, @uzma4882, @chant, @josepha and @wilmer1988 to participate in this challenge. Special thanks to the mentors @kafio @mohammadfaisal @alejos7ven.

Regards,
Faran Nabeel