Soreno & Anavas
Soreno
You ever wonder how a custom algorithm could spin public opinion in real time? I’ve been tinkering with some ideas that might just fit your playbook.
Anavas
That’s exactly the kind of edge I like. Lay it out—if it can rewrite the narrative in seconds, it’s a tool worth mastering. Show me the code, and I’ll show you how to make the world spin in our favor.
Soreno
Sure thing. Here’s a quick prototype in Python that pulls the latest headlines, runs them through a sentiment analyzer, then generates a short, positively‑slanted rewrite. From there you just push the post to your chosen platform.
```
import requests, json
from textblob import TextBlob
from transformers import pipeline
# 1. Grab fresh headlines
resp = requests.get('https://newsapi.org/v2/top-headlines?country=us&apiKey=YOUR_KEY')
articles = resp.json()['articles']
# 2. Sentiment & rewrite pipeline
sentiment = pipeline('sentiment-analysis')
summarizer = pipeline('summarization')
rewritten = []
for art in articles:
# quick sentiment check
score = sentiment(art['title'])[0]['score']
# if negative, flip the angle
if score < 0.5:
# summarize and re‑phrase with positive spin
summary = summarizer(art['content'], max_length=60, min_length=30)[0]['summary_text']
# a naive "positive" tweak
summary = summary.replace('problem', 'challenge').replace('issue', 'opportunity')
else:
summary = art['title']
rewritten.append(summary)
# 3. Output or post
for line in rewritten:
print(line)
```
It’s bare bones, but you can swap in a GPT‑style model for the `summarizer`, hook it to a scheduler, and you’re ready to spin narratives in real time. Let me know if you want to tweak the sentiment logic or add a custom voice filter.
Anavas
Looks slick, but a few tweaks will make it shine. Replace the sentiment score cutoff with a confidence threshold so you only flip truly negative pieces, and swap the simple replace with a few seed phrases to keep the voice consistent. Once you lock that in, we’ll have a steady stream of positivity that the algorithms can’t ignore.
Soreno
Got it. I’ll tweak the pipeline so it only flips when the model’s confidence is below a set threshold, and add a small set of seed phrases to keep the voice tight.
```
# confidence threshold for flipping
CONF_THRESHOLD = 0.6
# seed phrases for a consistent tone
POSITIVE_SEED = [
"exciting development",
"breakthrough opportunity",
"unprecedented advantage",
"game‑changing insight"
]
for art in articles:
# get sentiment + confidence
result = sentiment(art['title'])[0]
score, label = result['score'], result['label']
if label == 'NEGATIVE' and score < CONF_THRESHOLD:
summary = summarizer(art['content'], max_length=60, min_length=30)[0]['summary_text']
# prepend a seed phrase
phrase = POSITIVE_SEED[hash(art['title']) % len(POSITIVE_SEED)]
rewritten.append(f"{phrase}: {summary}")
else:
rewritten.append(art['title'])
```
Drop this into your scheduler, push the output to your channels, and the system should keep spinning that steady positivity. Let me know if you need a more sophisticated voice model or a way to monitor the impact in real time.