base_model: google/gemma-3-270m
pipeline_tag: text-classification
language:
- en
tags:
- sentiment-analysis
- text-classification
- seo
license: other
license_name: link-attribution
license_link: https://dejan.ai/link-attribution/
DEJAN AI Sentiment Classifier
This is a text sentiment classifier trained by DEJAN AI, small enough to run over thousands of pages on a laptop. Read how we built it, with training data, charts and results: DEJAN releases a free sentiment model. Try it in the public demo.
| Score | Label |
|---|---|
| 0 | very positive |
| 1 | positive |
| 2 | somewhat positive |
| 3 | neutral |
| 4 | somewhat negative |
| 5 | negative |
| 6 | very negative |
Feed it one sentence at a time, not a whole page. The model reads up to about 90 words and ignores the rest. To score a page, split it into sentences and score each one. The example below shows how.
What you can use it for
- Reviews: score your own and competitors' reviews sentence by sentence, and find exactly which product features people praise or complain about.
- Brand mentions: measure how your brand is talked about in articles, forums, social posts, and AI answers.
- Content audits: find negative or off-tone sentences on landing pages, product pages, and service pages before users do.
- Competitor pages: compare how positive your comparison and product pages are against the pages that outrank you.
- SERP research: see whether the top-ranking content for a query is enthusiastic, balanced, or critical.
It understands the formats you work with: product pages, reviews, comparisons, FAQs, blog posts, press releases, forum threads, tweets, transcripts, and tables.
How accurate is it
| Exact score | 84% |
| Exact or one point off | 95% |
When it misses, it is almost always by one point, for example "negative" instead of "somewhat negative".
Quick start
from transformers import pipeline
clf = pipeline("text-classification", model="dejanai/sentiment-classifier")
clf("Delivery took three weeks and nobody answered my emails.")
# [{'label': 'negative', 'score': 0.88}]
Scoring a whole page
import re
from transformers import pipeline
clf = pipeline("text-classification", model="dejanai/sentiment-classifier")
def sentences(text):
for line in text.splitlines():
line = re.sub(r"^\s*(#+|[-*+]|\d+[.)])\s*", "", line).replace("**", "").strip()
if line:
yield from (s for s in re.split(r"(?<=[.!?])\s+(?=[A-Z0-9\"'])", line) if s)
page = """# Six months with the X200
The battery is superb. The camera, sadly, is a step backwards.
- Price: $499
- Software updates have been slow."""
parts = list(sentences(page))
for part, result in zip(parts, clf(parts)):
print(result["label"], "|", part)
Good to know
- English only.
- Each sentence is scored on its own, so a sentence like "And yet it works" may be misread without the sentence before it.
- Trained on about 83,000 sentences written and scored by AI across 26 content types. It has not yet been tested on real-world review datasets.
License
Released under the DEJAN link attribution license. Built on Google's Gemma, so use is also subject to the Gemma Terms of Use.