How optimistic or pessimistic are the Kurzgesagt videos?

Everytime I watch a Kurzgesagt – In a Nutshell video, it makes me think whether I'm feeling optimistic or pessimistic about it. Perhaps it's because of all those stereotypical jokes and memes suggesting that Kurzgesagt videos induce existential crises.

This question prompted me to seek an answer, albeit with a naive approach. Therefore, I downloaded the transcript of the last 20 Kurzgesagt videos and fed it into the Vader Lexicon. The Vader Lexicon is a sentiment analysis tool that utilizes NLTK (Natural Language Toolkit) and operates based on a lexicon and rule-based approach.

TLDR

Here are the results:

Out of the 20 videos reviewed, 12 of them conveyed a positive feeling to the reader, while 8 of them had a more negative vibe than positive.
The average sentiment scores of the 20 reviewed videos were:
- Neutral: 76.55%
- Positive: 12.85%
- Negative: 10.6%

Implementation

It may not be technically accurate to claim that this naive approach measures the "optimistic" or "pessimistic" aspect of the videos. To determine whether something is pessimistic or optimistic, it is necessary to first establish a personal definition of what these terms mean. With that being said, feeding the entire transcript into the Vader using the approach mentioned still gives interesting results.

I have installed the vader_lexicon first:

python

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

nltk.download('vader_lexicon')

And then the transcript was fed into the polarity_scores function:

python

sia = SentimentIntensityAnalyzer()

video_data = []

for transcript_file in transcript_files:
    file_path = os.path.join("transcripts", transcript_file)
    with open(file_path, 'r', encoding="utf8") as json_file:
        data = json.load(json_file)
        transcript = " ".join(data)
        sentiment_scores = sia.polarity_scores(transcript)
        video_data.append([
            transcript_file,
            float_to_percentage(sentiment_scores["neu"]),
            float_to_percentage(sentiment_scores["pos"]),
            float_to_percentage(sentiment_scores["neg"]),
            "🔴 Negative" if sentiment_scores["compound"] < 0 else "🟢 Positive"
        ])

        print(f"Sentiment scores of '{transcript_file}': {sentiment_scores}")

pd.DataFrame(video_data, columns=["Transcript", "⚪ Neutral", "🟢 Positive", "🔴 Negative", "Compound"])

And here are the results:

Transcript	⚪ Neutral	🟢 Positive	🔴 Negative	Compound
black-hole-star-the-star-that-shouldn't-exist.json	84%	10%	6%	🟢 Positive
change-your-life-one-tiny-step-at-a-time.json	81%	12%	7%	🟢 Positive
how-to-terraform-mars-with-lasers.json	84%	11%	5%	🟢 Positive
how-we-make-money-on-youtube-with-20m-subs.json	78%	18%	4%	🟢 Positive
is-civilization-on-the-brink-of-collapse.json	74%	10%	16%	🔴 Negative
lets-travel-to-the-most-extreme-place-in-the-universe.json	84%	10%	6%	🟢 Positive
the-black-hole-that-kills-galaxies-quasars.json	83%	12%	5%	🟢 Positive
the-deadliest-virus-on-earth.json	74%	13%	13%	🔴 Negative
the-horror-of-the-slaver-ant.json	72%	12%	16%	🔴 Negative
the-last-human-a-glimpse-into-the-far-future.json	81%	12%	7%	🟢 Positive
the-most-complex-language-in-the-world.json	83%	10%	7%	🟢 Positive
the-most-dangerous-weapon-in-not-nuclear.json	72%	15%	13%	🟢 Positive
the-most-extreme-explosion-in-the-universe.json	80%	10%	10%	🔴 Negative
the-reason-why-cancer-is-so-hard-to-beat.json	61%	12%	27%	🔴 Negative
what-actually-happens-when-you-are-sick.json	69%	16%	15%	🔴 Negative
what-happens-if-a-supervolcano-blows-up.json	79%	14%	7%	🟢 Positive
why-aliens-might-already-be-on-their-way-to-us.json	84%	12%	5%	🟢 Positive
why-don't-we-shoot-nuclear-waste-into-space.json	74%	11%	14%	🔴 Negative
why-you-are-lonely-and-how-to-make-friends.json	67%	26%	7%	🟢 Positive
your-body-killed-cancer-5-minutes-ago.json	67%	11%	22%	🔴 Negative

Source Code

Source code is available at github.com/avestura/kurzgesagt-sentiment-analysis