I claim to love startups, to love entrepreneurship - but how do I define it?

Well, our friends at Google and OpenAI define it quite simply:

And although both these definitions are true, they avoid what startups are at their core.

At it’s core, a startup is made to solve a problem nobody has ever solved before, or to solve a problem better than the current person solving it.

- From the wise man himself, and yours truly, me - Noah

What a startup really is

Through following the principle that a startups’ core reason for existence is to solve the unsolved, it makes it so that if your truly “founding” a startup, your navigating in a constant unknown space - one that hasn’t been grasped by anything in humanity yet.

Because of this principle and belief, I view startups as its own inherent world.
A world that is constructed by all of societies problems.

This world is it’s own kind of utopia - a world that can’t ever be fully explored, because in order for us to have fully explored it, we would’ve had to solve every problem humanity faces, both now (present), and in the future.

And it’s just that, there will never be a time we stop facing problems as a society. Which means intrinsically, there will never be a time we have fully explored this “utopian world” of startups.

With that being said, what does it mean?

It means that there’s no playbook because no one’s ever been here before. There’s no set rules, there’s no guide, and there’s no built structure for when building a startup.

You’re building something nobody has ever done before. There is just you, and potentially the people you’ve brought along with you to go on about solving this problem.

How to Succeed & Navigate in a World of Unknown

OK. We’ve now settled down on a problem, how do you move forward? It’s pitch black, in this new world, where’s our flashlight? And how do we know we’re moving in the right direction?

IMO there’s three things you need:

1. Lifelong learning

2. The three mindsets to success (A “figure it out” (FIO) + antifragile mindset + beginner mindset)

3. Straight up luck — but both kinds

Each one of these stages to success come one after another, and have a particular order of which they come.

1. What is it to be a lifelong learner?

“In times of change the learners will inherit the earth” - Eric Hoffer

To learn is to keep on moving forward, it is to be adaptive for both the present, and for the future. By choosing to not learn, you’re choosing to do a great number of many other things; we call that opportunity cost.

The most dangerous outcome of refusing to learn, is that you will remain stagnant, to reach a peak and remain just that, until you erode and break.

If we focus on startups specifically, and study them, we can see that one of the most proprietary reasons giants fall is because they fail to adapt in volatile markets and times. A few companies come to mind when I think of this:

1. Blackberry

2. Blockbuster

3. MySpace

Blackberry especially, is an amazing case study on what happens when you don’t innovate, adapt, and refuse to learn.

Something else relevant to this context of being a “lifelong learner” is a quote my friend had mentioned the other day,

“Right now I’m the smartest I’ve ever been, and the dumbest I’ll ever be.”

I believe that if you want to reach some unconventional level of success, you have to live through this philosophy. Because through living in it, we humble ourselves, and remind ourselves that through learning, we will always be a beginner in some sort of way. And as a beginner, you gain a thirst to learn - which in turn, activates “The Three Mindsets.”

2. What the hell are “The Three Mindsets” ??

Great question.

The three mindsets are what follows after being a “lifelong learner,” they are mindsets that are naturally achieved from having such hunger for knowledge.

The three frames of mind include being a beginner, having a figure it out (FIO) approach, and an antifragile core.

You’ll see that through training each of these frames of mind, by virtue, greater things will occur almost by nature. So much that through doing so, the outcome of success builds upon itself and brings us to the next stage of creating your own luck (partially, but more on this after).

We’ll kick off with the first frame, being a beginner;
One of the GOATs said it best, the one and only Brian Chesky,

The bigger he gets, the more of a beginner he feels.

It took Picasso’s four years to paint like Raphael, but a lifetime to paint like a child. To be a beginner is to be curious, and that is to see the world with fresh eyes, to live // work without judgment, and never feeling like you’ve “made it.” Because once you believe you’ve arrived, you stop growing (which in turn is to stop learning - hopefully you get the connection and see the pattern).

Through being a beginner, we can see things that would’ve typically been overlooked. Because we are not blinded yet by expertise, assumptions, and our own arrogance.

When you’re new to something you question everything there is to it. You don’t accept “that’s how it’s always been done,” so you spot flaws veterans ignore. Whereas experts often filter reality. Experienced people see patterns - which lets them think quick, but also lead blindly. They skip over the depth of the issue because they assume the answer. Beginners will notice the awkward UX button, clunky onboarding process, the little friction, and so forth.

Those “naive” observations often lead to breakthrough design. After all, Brian Chesky was the one that said, “it’s in the tiny details that delight lives. Most people gloss over them. Beginners don’t.”

In correlation to this, something a good friend of mine recently said is “just when you reach the top of one mountain, you notice someone else standing in the clouds.”

You can’t have the mindset of believing that there even is an endpoint, or a peak; sure, you can be a big fish in a small pond, living on the mountain top.

It’s all about optimizing for what you want at the end of the day.

But I kinda want to see what the clouds are like, that’s for sure. Maybe I’ll see someone in space haha (hellooo Elon).

Figure it out (FIO) mindset:
In startups we don’t have the time, nor do we have the resources to not do the work. Simply put, if you don’t do the work, quickly and to a high standard, you will fail.

It is both a race and a marathon simultaneously.

The way we can possess a FIO mindset, is through holding such conviction in ourselves, that no matter what cards we’re handed, we trust ourselves to always find some sort of way to make ends meet, and finish the work needed.

The startup world moves quick, it’s speed is not relative to our time, and we cannot calculate it. This world can be thriving one day, and moving at an unprecedented pace - but then humanity can fail to keep up which leads to a bubble among many other things. It is an external factor to our success as founders, and all we can do is work within it, which is figuring things out is so important.

The greatest founders were amazing at “FIO” when it seemed impossible at face value. So many examples come to mind when talking about this, a great one is the beginning of DoorDash. Spun up “PaloAltoDelivery.com,” answered a Google Voice line, and manually delivered first orders.

Another good one is Reddit, the founders created tons of fake accounts to populate early posts/comments until real users arrived.

When Airbnb’s runway was getting short - they sold custom cereal boxes!! The opportunities are endless. You just have to think differently.

These are all great examples and reasons as to why you need to be thinking in a system where you refuse underperforming. You have to get the work done, it doesn’t matter - to any means possible.

There were so many opportunities to quit and fail in these examples listed, yet these amazing founders created opportunities out of nothing, and found the few things that could’ve actually worked out. All through thinking differently, and having the mindset that they could figure out whatever was thrown there way - the mindset capable for greatness.

Side note ::Would you believe me if I said my old LinkedIn bio used to say “Figure It Out Enthusiast”? So this one is held close to my heart… Hits home.

The antifragile core
The startup world is unpredictable. As mentioned before, it is a world of unknown. A world that can never be fully explored, as it is only somewhat explored by subject to the problems we know of - that have actually been solved.

Because of this unpredictability, we have to get comfortable with being in these unknown spaces - it’s an external environment that we cannot control, but in ways can manipulate it, and take advantage of the mass volatility.

This third mindset is derived fully from Antifragile: Things That Gain from Disorder, by Taleb - one of my favourite reads out there.

Taleb says, “Wind extinguishes a candle and energizes a fire. Likewise, with randomness, shock, and volatility: you want to be the fire, not the candle.”

Both the candle and fire cannot control the wind, it is an external force. However, they can place themselves in unique environments to grow, and thrive from the wind. This same philosophy applies to startups - we can use these external forces of being in an unknown environment, and take advantage of the volatility - not to scare from it, but to grow from it. Just as a fire would to the wind.

Through building an antifragile core, we can navigate the startup world to our best ability. Especially when combined with the other two mindsets of being a beginner, and to figure it out - we can combine these three systems to craft a great founder.

Luck in success + making your own (partially…)

There’s no doubt that luck plays a huge role in success - it’s in every aspect of winning in startups. Various factors like timing, people you meet, current economy, etc.

Instagram (or at that time “Burbn”) - literally raised there seed round from a16z and Baseline ventures from meeting them at a Hunch party.

Inversely, and more recently, Google got rid of the num=100 parameter - Reddit used to be a powerhouse in citations for AI. However with this new update, there rankings plummeted. Reddit’s citation share on ChatGPT had fell from approximately 29.2% to just 5.3%. In doing so, there stock tanked 15% in the span of 5 days - roughly $6.5B gone.

How could Reddit have seen that coming? They couldn’t. There’s just things out of anyones control because of the uncertainty.

It’s a big “right person, right time” game. There’s so many ways the “wind” could blow - that sometimes you get unlucky. Many can argue that since there are too many variables that goes into building a successful startup (worth $10M+), even if it’s not random, it’s too complex to be controlled.

And I agree with this, but to an extent.

There are external factors we cannot control, but that is in everything we do - you can be at a cushy corporate job, however a recession can occur and you can get laid off, or perhaps get passed on a promotion. I know is startups a far more volatile market than the corporate one - so luck plays a much larger factor, but you get the point.

I believe there are ways that we can make our own luck - I actually think a good portion of our luck is earned.

If we break down what luck is, it’s a fraction, odds. “How many times out of x number of times will I strike out?”

The odds of founding a unicorn through scaling a lemonade stand is minimal, yet the odds of founding a unicorn through scaling a startup in an interesting space, solving a genuine problem, with some sort of emerging technology, is a lot higher.

Not only that, but factors like how many startups you’ve worked on prior, or the number of people you’re working with play huge roles as well. This isn’t hyperbole either, it’s backed by real data.

According to First Round’s 10-Year Project, teams of more than one founder outperformed solo founders by 163%.

Ali Tamaseb, who analyzed 195 unicorns, the odds of succeeding nearly double when moving from a single-person team to a two-person or three-person:

I can go on, but the point I’m trying to make is that there are things we can do to increase our odds, and naturally, increase our luck. We create our own luck through building our own opportunities when there’s none to exist - I love the concept of “The Third Door” for this exact reason.

The third door talks about the two ways we can enter a club - you can wait in the long line with the normies, or alternatively you can go to the VIP line.

Couple problems,

A: it’s cold, and waiting in a line sucks.
B: we’re not cool enough to be in VIP.

So what do we do? We make the third door ourselves - whether that door be paying off the bouncer, kicking in a window at the bathroom, acting like a famous person, etc.

Opportunities are rare because they’re out of our control, but we can make them more common if we create them ourselves, therefore putting them within our control, and within our reach.

For instance, one of my friends needed to raise VC money » no VC would sit down with him » he ordered pizzas, dressed as a delivery guy, and put a QR code inside each box. Physically WENT to the VC offices and delivered them » when the VCs opened the lid, it linked to his demo, pitch deck, and one-pager.

He got a call back from every VC firm he went to. And then raised some money.

If you still want to build…

At the end of the day, startups aren’t about pitch decks, or seed rounds. It’s about choosing to build something in a place where nobody can tell you what to do, because nobody’s figured it out yet. A startup is just the tool - the real thing is deciding to live in uncertainty on purpose. As long as there are problems, there will be people trying to solve them. And I’d rather be out there trying, even in the dark, than sitting around waiting for someone else to turn on the lights.

If you made it this far, thank you! This essay in particular was especially long, so the time you’ve given me means so much more than you might realize. I’m sure your a very busy person.

I’m using this blog experiment to let people get to know me + what I believe - and hopefully connect with more of you along the way. If you have any questions about what I’ve done, or would just like the chat - hit me up at pointone@barbaros.ca - if I don’t get back within 24 hours, coffee’s on me.

Once again, thanks for your time.

With such a beginner level understanding, how did I create a robust Twitter Sentiment Analyzer that can:

• Fetch real-time tweets based on user-defined queries

• Clean and preprocess tweet text for analysis

• Analyze sentiments using natural language processing

• Visualize sentiment distribution with intuitive charts

• Save and debug processed data for future reference?

I’ll walk you through each step below! However feel free to check out my tutorial for a full walkthrough on how this works!

Before you start, ensure you have Python installed on your machine along with the necessary libraries: tweepy, textblob, matplotlib, and re. You can install these using pip:

pip install tweepy textblob matplotlib

Step 1: Loading Configuration

Configurations like API keys and default settings are crucial for accessing Twitter’s API and customizing the behavior of our analyzer.

Comparable to setting up your workspace, loading configurations ensures everything is in place before diving into the code.

import json

def load_config():
    with open("config.json", "r", encoding="utf-8") as f:
        data = f.read()
        print("File content:", data)  # Debugging: ensure the JSON is read correctly
        return json.loads(data)

Explanation:

• Purpose: Reads the config.json file containing sensitive information like API keys.

• Debugging: Prints the file content to verify correct reading (remove in production).

• JSON Parsing: Converts the JSON string into a Python dictionary for easy access.

Sample config.json:

{
    "bearer_token": "YOUR_TWITTER_BEARER_TOKEN",
    "default_tweet_count": 50,
    "polarity_positive_threshold": 0.2,
    "polarity_negative_threshold": -0.2
}

Visual Aid: Imagine a neatly organized toolbox where each tool has its designated place — this is what config.json achieves for your project.

Step 2: Authenticating with Twitter API

Authentication is like getting your ticket to access Twitter’s data. Without it, you can’t fetch any tweets.

import tweepy

def authenticate(config):
    # For Twitter API v2, we primarily use the bearer token
    client = tweepy.Client(bearer_token=config["bearer_token"])
    return client

Explanation:

• Tweepy Client: Utilizes the bearer token from the config to authenticate requests.

• Security: Keeps API keys secure by storing them in a separate configuration file.

Best Practice Tip: Never hard-code your API keys in your scripts. Always use environment variables or configuration files excluded from version control.

Step 3: Fetching Tweets

Fetching tweets based on user-defined queries is the core of our analyzer.

def fetch_tweets(client, query, count):
    # Twitter API v2: search_recent_tweets can fetch up to 100 tweets per request
    max_results = min(count, 100)
    # Append `lang:en` to the query to filter English tweets
    query = f"{query} lang:en"
    response = client.search_recent_tweets(query=query, max_results=max_results, tweet_fields=['text'])
    return response.data if response.data else []

Explanation:

• Query Parameters: Filters tweets in English to maintain consistency in analysis.

• Rate Limits: Caps the number of tweets per request to 100, adhering to Twitter’s API limits.

• Error Handling: Returns an empty list if no tweets are found to prevent crashes.

Visual Comparison:

Before Fetching:

[Empty]

After Fetching:

["I love sunny days!", "Feeling sad about the news...", ...]

Step 4: Preprocessing Tweets

Raw tweet data can be messy. Preprocessing cleans the text, making it suitable for sentiment analysis.

import re
def preprocess_tweet(text):
    text = re.sub(r'RT\s+', '', text)           # Remove retweets
    text = re.sub(r'@\w+', '', text)           # Remove mentions
    text = re.sub(r'http\S+', '', text)        # Remove URLs
    text = re.sub(r'\s+', ' ', text).strip()   # Remove extra whitespace
    return text

Explanation:

• Regex Patterns: Efficiently removes unwanted parts of the tweet.

• Clean Text: Ensures the sentiment analysis focuses solely on meaningful content.

Analogy: Think of preprocessing as cleaning ingredients before cooking — removing impurities to enhance the final dish’s flavor.

Step 5: Analyzing Sentiment

Using natural language processing to determine the sentiment of each tweet.

from textblob import TextBlob

def analyze_sentiment(tweets, pos_threshold, neg_threshold, debug=False):
    positive_count = 0
    neutral_count = 0
    negative_count = 0
    processed_tweets = []
    for tweet in tweets:
        cleaned_text = preprocess_tweet(tweet.text)
        blob = TextBlob(cleaned_text)
        polarity = blob.sentiment.polarity
        if polarity > pos_threshold:
            positive_count += 1
        elif polarity < neg_threshold:
            negative_count += 1
        else:
            neutral_count += 1
        if debug:
            processed_tweets.append((cleaned_text, polarity))
    return positive_count, neutral_count, negative_count, processed_tweets

Explanation:

• TextBlob: Analyzes the polarity of the tweet, ranging from -1 (negative) to +1 (positive).

• Thresholds: Customizable to define what constitutes positive, neutral, or negative sentiments.

• Debugging: Optionally stores processed tweets and their polarity for further inspection.

Visual Aid: Imagine a scale where tweets are placed based on their sentiment score — some leaning towards positive, some neutral, and others negative.

Step 6: Plotting Results

Visualizing the sentiment distribution helps in quickly understanding the overall mood of the tweets.

import matplotlib.pyplot as plt

def plot_results(positive, neutral, negative):
    labels = ['Positive', 'Neutral', 'Negative']
    counts = [positive, neutral, negative]
    colors = ['green', 'blue', 'red']
    
    plt.figure(figsize=(8, 6))
    plt.bar(labels, counts, color=colors)
    plt.title('Sentiment Distribution')
    plt.ylabel('Count')
    plt.xlabel('Sentiment')
    plt.ylim(0, max(counts) + 10)
    
    for i, count in enumerate(counts):
        plt.text(i, count + 1, str(count), ha='center', va='bottom')
    
    plt.show()

Explanation:

• Bar Chart: Clearly displays the number of positive, neutral, and negative tweets.

• Customization: Colors and labels enhance readability and visual appeal.

• Annotations: Adds count labels above each bar for precise information.

Best Practice Tip: Always label your axes and provide a title to make charts self-explanatory.

Step 7: Running the Main Function

Integrating all components to execute the sentiment analysis workflow.

def main():
    config = load_config()
    client = authenticate(config)

query = input("Enter a search term: ")
    count_input = input(f"Number of tweets to fetch (default: {config['default_tweet_count']}): ")
    try:
        count = int(count_input) if count_input.strip() else config["default_tweet_count"]
    except ValueError:
        print("Invalid input. Using default tweet count.")
        count = config["default_tweet_count"]
    tweets = fetch_tweets(client, query, count)
    if not tweets:
        print("No tweets found. Please try a different query.")
        return
    positive, neutral, negative, _ = analyze_sentiment(
        tweets,
        pos_threshold=config.get("polarity_positive_threshold", 0.2),
        neg_threshold=config.get("polarity_negative_threshold", -0.2),
        debug=False
    )
    print("\nSentiment Analysis Results:")
    print(f"Positive: {positive}")
    print(f"Neutral: {neutral}")
    print(f"Negative: {negative}")
    show_chart = input("\nShow chart? (y/n): ")
    if show_chart.lower() == 'y':
        plot_results(positive, neutral, negative)
    debug_print = input("Print cleaned tweets for debugging? (y/n): ")
    if debug_print.lower() == 'y':
        _, _, _, debug_tweets = analyze_sentiment(
            tweets,
            pos_threshold=config.get("polarity_positive_threshold", 0.2),
            neg_threshold=config.get("polarity_negative_threshold", -0.2),
            debug=True
        )
        print("\nSample Cleaned Tweets:")
        for t, p in debug_tweets[:10]:
            print(f"\"{t}\" - Polarity: {p}")

Explanation:

• User Interaction: Prompts users to input search terms and desired tweet counts.

• Error Handling: Validates user input and handles scenarios where no tweets are found.

• Conditional Execution: Offers options to display charts and debug information based on user preference.

• Sample Output: Displays a subset of cleaned tweets with their polarity scores for deeper insights.

Best Practice Tip: Always validate user inputs to make your program robust against unexpected or invalid data.

Conclusion

Building a Twitter Sentiment Analyzer from scratch was an enlightening experience that bridged my understanding of data fetching, natural language processing, and data visualization. By integrating Python’s powerful libraries — Tweepy, TextBlob, and Matplotlib — I was able to create a tool that not only analyzes sentiments but also presents the data in an intuitive manner.

What Did I Learn?

• API Integration: Gained hands-on experience with Twitter’s API using Tweepy, understanding how to authenticate and fetch real-time data.

• Data Cleaning: Learned the importance of preprocessing raw data to ensure accurate sentiment analysis.

• Natural Language Processing: Utilized TextBlob to analyze and interpret the sentiment of textual data effectively.

• Data Visualization: Enhanced my ability to present data through clear and informative charts using Matplotlib.

• Error Handling: Implemented robust error handling to make the application more user-friendly and resilient.

What’s the Bigger Picture?

In an era where data drives decisions, sentiment analysis tools like this one can be invaluable for businesses, researchers, and individuals seeking to gauge public opinion or emotional trends. Imagine scaling this project to:

• Real-Time Monitoring: Integrate with live data streams to monitor sentiment in real-time.

• Advanced Analytics: Incorporate machine learning models for more nuanced sentiment detection.

• User Interface: Develop a web-based interface for broader accessibility and user interaction.

• Multi-Language Support: Expand the analyzer to handle tweets in multiple languages, increasing its global applicability.

Future Enhancements:

• Subscription Models: Offer premium features such as historical data analysis, detailed reports, and personalized dashboards.

• Integration with Other Platforms: Extend the tool to analyze sentiments across various social media platforms like Facebook, Instagram, and Reddit.

• AI and Machine Learning: Leverage more sophisticated models to improve sentiment accuracy and context understanding.

Thank You,

This is an old project of mine from ~ a year ago! It was originally posted on Medium but a good friend told me it helps to cross-post. If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so the time you’ve given me means a lot more than you might realize.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

---

Additional Resources

Source Code (Github Repo)

Twitter Developer Portal

Tweepy for interacting with the Twitter API

TextBlob for sentiment analysis

Matplotlib for data visualization

Regex (re) for text preprocessing

We like to think we’re rational investors, driven by data and diligent analysis. But let’s be real: markets don’t just move on numbers — they swing wildly on emotion, hype, and headlines. Think about it. One tweet, one scandal, one new product launch, and a company’s stock can skyrocket or tank, sometimes for reasons that have nothing to do with the balance sheet. This is the hidden power of sentiment in the stock market: it’s not about what is happening, but how people feel about it.

In a world where perception often outweighs reality, the ability to read market sentiment becomes a critical skill. That’s where this project steps in. With a unique combination of sentiment analysis and real-time data scraping, we’re capturing the heartbeat of the market for companies like Amazon, Google, and Meta— seeing beyond the financial reports to understand the emotion driving the prices.

Whether you’re an investor looking for an edge or simply fascinated by the psychology of finance, this project lets you see the market as more than numbers on a screen. Get ready to dive into the minds of the market movers and see what makes prices tick.

TL;DR:

Sentiment analysis tools like VADER provide a powerful yet imperfect solution for extracting emotional insights from text data. While capable of classifying sentiments as positive or negative, they can fall short in scenarios involving sarcasm or conflicting language.

A Classical Approach to Stock Analysis

In the world of traditional investing, tracking a stock’s sentiment means staying updated with news, reports, and analyst opinions — all of which take considerable time to read and interpret. Often, investors rely on financial advisors, news summaries, or quantitative analysis platforms to stay informed, but each of these is either time-consuming or expensive. What if we could combine the power of natural language processing (NLP) and sentiment analysis to speed up this process?

Manual Sentiment Analysis: A Tedious Process

A manual approach to reading stock sentiment looks something like this:

1. Read the Headline: Manually go through each piece of financial news.

2. Interpret the Tone: Decide if the news is positive, neutral, or negative.

3. Record the Sentiment: Track these interpretations in a database.

This method quickly becomes impractical. The speed and sheer volume of financial news demand a faster, more automated solution.

Sentiment Analysis with VADER: A Modern Solution

Enter VADER (Valence Aware Dictionary and sEntiment Reasoner). VADER is an NLP tool specifically designed for analyzing the sentiment of short texts, making it ideal for headlines. Here’s how VADER transforms headlines into scores:

• Positive, Neutral, and Negative Scoring: Each headline is broken down into scores across positive, negative, and neutral categories.

• Compound Score: The overall sentiment is expressed as a compound score ranging from -1 to +1, with positive scores indicating optimism and negative scores signaling concern.

Let’s consider a basic example:

• A headline like “Amazon breaks sales records this quarter” might receive a high positive score.

• In contrast, “Market challenges affect Meta’s profits” could score negatively.

VADER’s algorithm sorts these scores, allowing us to quickly see which companies are trending positively or negatively in the market.

The Naive Bayes Algorithm: The Engine Behind Sentiment Analysis

Our project taps into the fundamentals of sentiment analysis through a statistical approach known as the Naive Bayes Algorithm. This algorithm, grounded in probability, predicts whether a headline is likely to impact a stock positively or negatively. It works by evaluating the likelihood of certain words or phrases appearing in either positive or negative reviews based on historical data.

In this model, we let “X” represent all the headlines or news snippets, while “Y” becomes the final output score, where a positive score indicates an optimistic sentiment and a negative one a pessimistic sentiment. Here’s a look at the process:

• P(Y=Positive | X) gives us the probability that the market sentiment is positive given a particular headline.

• P(Y=Negative | X) does the same for negative sentiment, allowing us to gauge the influence of news on a stock’s public perception.

VADER: Quick, Rule-Based Sentiment Scoring

We use VADER (Valence Aware Dictionary and sEntiment Reasoner), a rule-based model built for general sentiment analysis. VADER scores text for positive, neutral, negative, and compound (overall) sentiment. The beauty of VADER is its efficiency: it doesn’t analyze every word but focuses on those that carry emotional weight, skipping filler words like “the” or “and.”

This tool enables us to calculate sentiment on a massive scale — quickly scoring each headline without heavy computational demands.

Web Scraping and Real-Time Analysis

Of course, analyzing sentiment would be meaningless without real-time data. Using Python’s web scraping tools, we fetch live headlines and stock news directly from Finviz, a leading financial website. Our scraper identifies and extracts headlines tied to specific tickers — AMZN, GOOG, META— pulling them into our analysis pipeline. By examining the latest news, we can dynamically update the sentiment score based on recent events.

Our project then structures the scraped data, linking each headline with its timestamp and sentiment score to create a live snapshot of how the market feels about each company. The final result? An insightful, constantly updating bar chart displaying sentiment over time for each stock.

Coding the Project: Step-by-Step Implementation

This section breaks down how I coded a simple sentiment analysis tool that scrapes headlines from Finviz, analyzes them with VADER, and visualizes the results.

Step 1: Data Collection — Gathering Headlines from Finviz

The first part of our process involves collecting data. For this project, we pull stock headlines directly from Finviz for tickers like Amazon (AMZN), Google (GOOG), and Meta (META), which serve as the foundation for our sentiment analysis.

Here’s the code for scraping headlines:

from urllib.request import urlopen, Request
from bs4 import BeautifulSoup

tickers = ['AMZN', 'GOOG', 'META']
finviz_url = 'https://finviz.com/quote.ashx?t='news_tables = {}
for ticker in tickers:
    url = finviz_url + ticker
    req = Request(url=url, headers={'user-agent': 'my-app'})
    response = urlopen(req)
    html = BeautifulSoup(response, 'html.parser')
    news_table = html.find(id='news-table')
    news_tables[ticker] = news_table

We start with a request to Finviz for each ticker symbol. Once we gather the HTML data, we use BeautifulSoup to isolate the table containing each company’s news. This table is stored for further processing, ready to be analyzed for sentiment.

Step 2: Applying Sentiment Analysis with VADER

Once we’ve gathered the news data, we dive into sentiment analysis using VADER. VADER, or Valence Aware Dictionary and sEntiment Reasoner, provides a compound sentiment score that categorizes text as positive, negative, or neutral.

Here’s the code for processing sentiment:

from nltk.sentiment.vader import SentimentIntensityAnalyzer
import pandas as pd

parsed_data = []
vader = SentimentIntensityAnalyzer()for ticker, news_table in news_tables.items():
    for row in news_table.findAll('tr'):
        title = row.a.text.strip()
        date_data = row.td.text.strip().split(' ')
        date = date_data[0]
        time = date_data[1] if len(date_data) > 1 else 'N/A'
        compound = vader.polarity_scores(title)['compound']
        parsed_data.append([ticker, date, time, title, compound])df = pd.DataFrame(parsed_data, columns=['ticker', 'date', 'time', 'title', 'compound'])

This step applies VADER’s compound score to each headline, converting sentiment into a numeric value that is easy to interpret: positive, negative, or neutral. We now have structured data that reflects the emotional tone behind each headline, making it possible to track shifts in market sentiment over time.

Step 3: Visualizing the Results

Finally, we visualize our sentiment scores to reveal trends and patterns. This step involves grouping data by date, calculating average sentiment for each stock, and plotting it as a bar graph to showcase sentiment shifts over time.

Here’s how we generate this visual insight:

import matplotlib.pyplot as plt

df['date'] = pd.to_datetime(df['date'])
mean_df = df.groupby(['ticker', 'date']).mean().unstack()
mean_df = mean_df.xs('compound', axis="columns")
mean_df.plot(kind='bar')
plt.title("Daily Sentiment for Stocks")
plt.xlabel("Date")
plt.ylabel("Mean Compound Sentiment Score")
plt.show()

The bar plot effectively communicates sentiment shifts by date and ticker. A positive bar height indicates optimism, while a negative bar height signals caution or pessimism — making it easy to identify which stocks are receiving favorable or unfavorable attention

AI isn’t perfect

AI is powerful, but it’s not perfect. There are times when sentiment analysis models misinterpret the tone of reviews. For example, a headline may earn a sentiment score that doesn’t align with the true intent behind the text, leading to results that may not accurately reflect the market’s mood. This becomes especially evident in cases of sarcasm or complex phrasing, where the literal words don’t match the implied sentiment.

Let’s look at some cases where sentiment scores and actual sentiment diverge:

# Identifying positive sentiment in news that should be negative
positive_sentiment_negative_review = df.query('compound > 0 and title.str.contains("loss|decline|problem")', engine='python')
print(positive_sentiment_negative_review[['title', 'compound']].head(3))

# Identifying negative sentiment in news that should be positive
negative_sentiment_positive_review = df.query('compound < 0 and title.str.contains("growth|gain|success")', engine='python')
print(negative_sentiment_positive_review[['title', 'compound']].head(3))

By querying for specific keywords that may contradict the sentiment score, we can identify examples of sentiment discrepancies. Here’s an example of where this might occur:

• “Company reports strong growth, but a decline in specific sectors”: VADER might score this as positive due to keywords like strong and growth, despite the mention of decline, which tempers the news sentiment.

• “Revenue gains lead to success, although facing minor issues”: The model may interpret success and gains positively, overlooking the sentiment-shifting impact of minor issues.

These nuances illustrate how AI models, including VADER, can stumble with sentiment detection due to the inability to gauge context fully, especially for nuanced expressions like sarcasm.

Thank You,

This is an old project of mine from ~ a year ago! It was originally posted on Medium but a good friend told me it helps to cross-post. If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so your time means more than you would know to me.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

It’s a pretty self explanatory post — but eye awakening. Why is it that it took us 68 years to reach a terawatt of solar, yet just 2 years to double it?

The answer’s simple, compounding — and it applies to human potential just as much, if not more.

"The First Step is the Hardest"

I’m sure we’re all familiar with this phrase;

And it couldn’t be more true. The hardest part in developing human growth is the first breakthrough — learning a skill, building credibility, or getting that first win.

Building the foundation.

Early progress especially, feels painfully slow because your creating an entirely new framework — things that were not at all once there, things that are new to you: habits, knowledge, systems, and confidence, the list goes on.

Infrastructure and Flywheels + Doubling Potential

Once that basis // framework is established, effort compounds. The system of success (network, skills, reputation, resources) works like solar manufacturing at scale: cost declines, and the process gets quicker.

With each iteration, you uncover flaws in what once seemed brilliant and refine systems that once felt revolutionary.

Each new step builds on what came before, so doubling becomes both faster and easier.

The first $10K saved is slow, the next $10K is probably still slow… but a bit faster haha.

This isn’t hyperbole either; Theodore Paul Wright, was a U.S. aeronautical engineer and educator, and he made up this law — called “Wright’s Law” (probably could’ve guessed that).

This law can be found in this lithium-ion batteries example:

In the early 2000s, every doubling of production took years. Costs were high, adoption was slow, and scaling felt painful. But as production piled up, the curve bent — doubling came faster, costs fell, and batteries suddenly seemed to be “everywhere.”

Compounding occurred.

Wright’s Law explains why solar, batteries, and other technologies scale so fast once they break through. Every doubling of production drives costs down, making adoption easier, which fuels the next doubling. That’s why the second terawatt of solar took 2 years instead of 68.

Now zoom out: humans operate on a similar curve. Our currency, in “cost” of achieving something (whether it be in energy, effort, time) declines with each doubling of experience.

The relative cost per unit of progress drops. You’ve built the systems, habits, and pattern recognition that make each additional doubling faster. Just like solar panels get cheaper the more we produce, your “unit cost” of achievement falls the more you practice and accumulate wins.

Building becomes easier and growth becomes exponential.

Thank You,

These are my latest thoughts! If you took the time to read all of this, I want to say thank you for your time — I’m sure your a very busy person, so your time means so much to me.

I want to use this blog experiment as a way for people to learn more about me and, hopefully, connect with more people. If you have any questions about what I’ve done, or would just like the chat — hit me up at pointone@barbaros.ca — if I don’t get back within 24 hours, coffee’s on me.

After personally creating content for businesses that consistently gets featured in Google’s AI Overviews and cited by leading language models like ChatGPT, Perplexity, and Claude, I’ve identified the key strategies that drive both AI visibility and human engagement. The shift from traditional SEO to AIO (AI Optimization) / GEO (Generative Engine Optimization) requires a fundamental rethinking of content strategy.

Here’s what we’ll cover: crafting content that AI systems prioritize and how to leverage the right optimization tools.

TL;DR: What I Learned Getting My Content Cited by AI

After getting cited in Google’s AI Overviews, Perplexity, and ChatGPT, I found a clear pattern:
AI systems reward structured, scannable, and trust-rich content — not keyword dumps or long-winded narratives.

The key drivers?

• Answer-first formatting

• Credibility signals within the first 200 words

• Brand authority over backlinks

• Schema, sentiment, and multimodal presence (especially video)

Bottom line: The brands getting cited aren’t necessarily creating better content — they’re creating content that matches how AI systems are designed to extract and reference information.

This article breaks down the exact playbook I use to get my content featured by AI, while still making it valuable for human readers.

Writing Content That Both Humans & AI Systems LOVE

To crack this code, we first need to understand how and why AI systems cite specific sources. If you’re interested in the technical breakdown of the inference stage in AI, I’ve covered that in detail elsewhere.

Build “Answer-First” Pages

Here’s what I’ve learned: AI systems favour content that’s concise, direct, and scannable — qualities that also happen to drive human engagement.

The key is front loading your answers (ie. incorporating a TL;DR at the beginning). Instead of burying insights deep in paragraphs, lead with the solution, then provide supporting context. This “answer-first” approach satisfies both AI’s need for quick pattern recognition and human readers’ desire for immediate value.

The most effective formats include:

• TL;DR at the top of content

• FAQ sections

• Structured how-to guides

• Definition-heavy content that provides immediate value

AI models process structured content more effectively than dense paragraphs. The formats that consistently get cited include descriptive headings that telegraph content, FAQ sections that directly answer common queries, step-by-step processes that break down complex procedures, and bullet points that highlight key information.

This isn’t coincidence — it’s how AI systems are trained to extract information. Google’s AI Overviews frequently pull from FAQ schema, while Perplexity and Claude often reproduce well-formatted lists and step-by-step guides. By structuring your content to match these patterns, you’re essentially speaking the AI’s native language while simultaneously improving human readability.

The Clarity Advantage

Content clarity directly correlates with AI citation rates. Through testing, I’ve found that simplifying language and improving readability consistently outperforms keyword-heavy approaches. AI systems struggle with jargon-dense content the same way humans do — they favour clear explanations over technical complexity.

This means writing shorter sentences, explaining concepts in plain language, and maintaining strong grammatical flow. The goal isn’t dumbing down your content; it’s making expertise accessible. When you can explain complex ideas simply, both AI systems and human readers gravitate toward your content as the definitive source.

Building Trust Signals That AI Systems Recognize

Trust isn’t just about human perception — AI systems actively evaluate credibility markers when selecting content to cite. Through extensive testing, I’ve identified the specific signals that boost both AI visibility and human confidence.

Strategic Citation Integration

Content that references external sources receives significantly higher AI selection rates.

But there’s a methodology here:

• Cite authoritative sources within the first 200 words

• Use diverse source types (academic papers, industry reports, reputable publications)

• Ensure citations directly support your core arguments.

Expert Voice Integration

Direct quotes from recognized authorities create immediate credibility signals. The most effective approach involves weaving expert perspectives throughout your content, not just dropping them in as isolated blocks.

For instance, a cybersecurity article that integrates insights from security researchers, official reports, and industry leaders throughout the narrative demonstrates comprehensive sourcing that AI systems recognize and prioritize.

Data-Driven Substantiation

Statistical evidence serves as content currency in the AI economy. Specific numbers, percentages, and research findings provide concrete value that AI systems can extract and reference. The key is presenting data that’s both current and contextually relevant — “95% of users reported improved efficiency” carries more weight than vague claims about “significant improvements.”

Technical Depth Without Density

Including relevant industry terminology and technical concepts signals topical authority to AI systems. In cloud computing content, naturally incorporating terms like Kubernetes, REST APIs, and containerization demonstrates subject matter expertise. The critical balance is maintaining readability while showcasing depth — technical accuracy that doesn’t sacrifice accessibility.

E-E-A-T Optimization for AI

Experience, Expertise, Authoritativeness, and Trustworthiness aren’t just Google ranking factors — they’re AI selection criteria.

This means:

• Clear author credentials

• Demonstrated first-hand experience

• Confident, authoritative tone throughout your content

AI systems favor content that reads as expert-written and thoroughly researched.

Digital Ecosystem Presence

Perhaps most importantly, AI systems heavily weight cross-platform mentions and discussions. Your content’s likelihood of being cited increases dramatically when your brand or insights appear across forums, social media, and industry discussions.

This means strategic community engagement and thought leadership efforts directly impact AI visibility — a factor many content creators overlook.

Review Velocity as Trust Currency

AI systems perform sentiment analysis on your brand’s digital footprint, making reviews a critical ranking factor.

The combination of review count, velocity, and quality across platforms like Yelp, Google Business, and industry-specific sites creates powerful trust signals. LLMs actively crawl these review sources to assess credibility, meaning your off-page reputation directly influences AI citation rates.

Brand Authority > Backlinks

Unlike traditional SEO, AI systems prioritize brand authority and topical relevance over link building. Mentions in trusted sources — even without direct links — shape how AI models summarize and cite your content. This represents a fundamental shift where earned media and thought leadership carry more weight than traditional link acquisition strategies.

Stack That Got Me into AI Overviews

Traditional SEO tools weren’t built for AI optimization. While keyword research and backlink analysis remain useful, the metrics that matter most for AI visibility require entirely different measurement approaches. After testing dozens of tools and platforms, I’ve identified the essential stack that drives real AI citation results.

Schema Markup and Structured Data Implementation

Just as traditional Google algorithms rely on clear signals, AI systems need structured data to interpret your content effectively. Schema markup serves as a direct communication channel with LLMs, providing context that dramatically improves citation rates.

As mentioned earlier, the most impactful schema types for AI optimization include FAQ schema for question-based queries, How-to schema for process-driven content, Review schema for trust signals, and Organization schema for brand authority. Tools like Schema Pro, Structured Data Markup Helper, and JSON-LD generators streamline implementation, but the key is strategic deployment rather than comprehensive coverage.

Content Optimization for AI Readability

Earlier, I mentioned the importance of clarity — something we can actively test and improve. Tools like Grammarly offer baseline readability checks, but optimizing for AI requires deeper analysis. I often use LLMs themselves (specifically Claude) to fine-tune grammar, tone, and structure for AI readability.

YouTube Optimization for AI Visibility

Gemini heavily favors YouTube content in AI Overviews, making video optimization a critical component of AI strategy.

Focus on short, educational videos tied to core queries, optimized titles that match conversational search patterns, and strategic keyword placement in descriptions and transcripts. The goal isn’t viral reach — it’s becoming the authoritative video source that AI systems cite when answering specific questions.

The Future is Already Here

The shift from traditional SEO to AI optimization isn’t coming — it’s already happened. While most marketers are still optimizing for search engines from 2019, the brands getting cited by AI systems today are the ones that will dominate tomorrow’s search landscape.

The playbook is straightforward: structure your content like AI systems are trained to process it, build trust signals that algorithms can recognize, and optimize for the tools that actually matter in an AI-first world. It’s not about gaming the system — it’s about understanding how information flows in the age of generative AI.

What we’ve covered here — from answer-first formatting to schema implementation to cross-platform brand building — these aren’t advanced tactics. They’re foundational strategies that will only become more important as AI systems become the primary way people discover and consume information.

The businesses that adapt to this reality now will have a massive advantage. The ones that don’t will find themselves invisible in an AI-driven search world.

Let’s Keep The Conversation Going!

Hey! I’m Noah, a 17 y/o who’s been deep in the GEO space for the past year, where I’ve been running Antifragility Labs to help brands rank in both search engines and AI systems.

If you found this useful or have questions about any of these strategies, feel free to reach out. I love meeting people who are thinking about these problems and always enjoy swapping notes on what’s working in the AI optimization world.

The future of content is being written right now, and the more of us who understand it, the better off we’ll all be.

Thanks for reading this post! I love talking to you guys—if you have any questions, opinions, or just wanna chat, hit up my email at: noah@barbaros.ca anytime or comment below!

The AI wave has fundamentally shifted how we work, communicate, and live. But here's what nobody talks about: the magnitude of AI's risk matches the magnitude of its promise.

The problem isn't the technology itself - it's that we don't understand its strengths and weaknesses, and we're not leveraging what makes us uniquely human: judgment, taste, and our ability to handle the unknown.

Let me explain: every innovation exists for one reason: to solve problems - that's the whole point. But I believe there are two types of “innovation”:

“Regular innovation,” solves specific problems for specific people. “Disruptive innovation” solves problems for everyone. AI didn't just impact a niche group of people, it impacted everyone.

That's incredible. So very few things accomplish that. AI will go on to do so much good for the world - areas like cancer research will improve at a multiplier in contrast to before. I mean, people use AI for everything in their day-to-day lives asking prompts like:

“Make me an image on xyz”

“Write me this essay on abc”

“Draft my breakup text but make me sound like the mature one.”

“What’s the fastest way to lose weight?"

AI is revolutionizing cancer research… and also helping Chad figure out what to text his ex Rachel.

Jokes aside, at face value AI is great - “i can get amazing results without thinking?? noice.”

But behind said ‘face value’ we find a world where AI replaces the human mind, and that, is a very dangerous world.

What happens to the very things that make us human?

We’re in a time that AI is being frequently used to replace the human mind - replace our critical thinking, creativity, imagination.

AI is reshaping who we are.

And it’s happening everywhere. We have tools that could make us superhuman, but instead we're using them to think less, solve fewer problems ourselves, and basically cruise through life on autopilot in “zombie mode”, because these innovations “think” for us. It almost feels like we’re one step away from asking an LLM “how do i breathe again?”

Now don’t get me wrong, a lot of people are absolutely crushing it with AI. But most of us are using revolutionary technology to maintain comfortable mediocrity instead of pushing the boundaries of what "normal" looks like.

And that's slowly killing innovation itself.

Why is This Happening?

Human psychology.
The tools changed, but we didn’t.

A couple months ago, MIT released this study. It’s about how our brains work when using AI to write essays (which in my opinion… is the highest level of critical thinking). The results reveal exactly why we’re headed for a world where AI makes us stupid.

We're wired to conserve effort - why work harder when you can work smarter? When AI can write your emails, why learn to write better? When GPS handles navigation, why memorize routes? It makes perfect sense from an individual perspective.

EEG data from that MIT study backs this up. EEG (think a heart‑rate monitor for your neurons) showed that frontal‑theta (big‑picture planning) and beta bands (active thinking/problem‑solving) all but flatlined when people wrote with ChatGPT. Literal proof we work less hard once the bot steps in.

But here's the thing: we don't measure ourselves against our potential, we measure ourselves against everyone else. As long as we're keeping up with our peers, we feel contempt with our output.

So when everyone starts using the same shortcuts, the whole baseline shifts down together.

“Brain connectivity systematically scaled down with the amount of external support: the Brain‑only group exhibited the strongest, widest‑ranging networks, Search Engine group showed intermediate engagement, and LLM assistance elicited the weakest overall coupling.”

From the same experiment: brain engagement scaled Brain‑only > Google > ChatGPT. When every student grabbed the shortcut, the whole neural curve sank together - exactly the collective mediocrity Jim Rohn warned about.

It's like muscle atrophy, but for your brain. Stop using a skill, lose the skill. Except now we're outsourcing thinking itself to machines, and we're all getting mentally weaker without realizing it. The researchers literally call it planning off‑load: alpha/theta activity (linked to structuring thoughts and memory) disappeared once the LLM took over.

“The LLM group also fell behind in their ability to quote from the essays they wrote just minutes prior.”

83 % of the ChatGPT group couldn’t quote a single sentence they’d just written, while the ‘brain‑only’ writers rattled theirs off without thinking. That’s cognitive muscle loss in real time.

The scariest part? We think we're getting smarter because we can access more information and solve problems faster - 17/18 ChatGPT users still rated their essays ‘great.’

Access != understanding (haha get it…), and using tools isn't the same as developing capability.

Final Words

When we rely on technology to think for us, we lose critical thinking, creativity, and the humanity of who people are. We become consumers of solutions rather than creators of them. I’m not trying to preach “AI is the end of the world”, what I’m trying to share is that technology is very strong - imagine how much stronger it could be in addition to our critical thinking, creativity, and humanity, not as a replacement for them.

This use of technology is exactly how the world's most effective startups were made. The founders didn't just use existing tools to automate their way to success. They combined innovative technology with uniquely human insights - understanding problems that data couldn't capture, making creative connections that algorithms couldn't see, and building solutions that felt genuinely human.

Steve Jobs didn't just use computers; he reimagined what computers could be by applying taste and design thinking that no machine could replicate. The best AI companies today aren't just training bigger models - they're figuring out how to make AI actually useful by understanding human psychology and behaviour in ways that pure computation can't.

The opportunity isn't to let technology do everything for us. It's to use technology to amplify what makes us human, not replace it. When we do that, we don't just get better results - we get results that were impossible before.

We need to begin approaching these innovations with a sense of urgency, and ambition. Human potential is at the highest it has ever been - yet we're settling for the same results our grandparents got with way less powerful tools.

**Full disclaimer: I’m a 17 y/o and all of these points made are subject to change as I take on more life experiences. These essays are published as a way for me to push my current ideas out there as a true beginner.**

Do you disagree? If so, great! Reach out to me :: noah@barbaros.ca

I’m a 17 y/o -- the timing couldn’t be better to be wrong about things + learn! I’m not looking for a debate - I’m looking for new perspectives and ideologies. I’d love to chat.