A sentiment analyzer measures the emotional tone of text — whether it's positive, negative, or neutral. Paste any text below to see word-by-word scoring using the AFINN-165 lexicon, a widely used dataset of 3,300+ English words with sentiment ratings.
Input Text
Overall Sentiment
Word Scores
| Word | Score | Count |
|---|
Highlighted Text
How to Analyze Text Sentiment
Sentiment analysis measures whether text is emotionally positive, negative, or neutral. This tool uses the AFINN-165 lexicon to assign numeric scores to recognized words and calculate an overall sentiment rating.
Step 1: Paste Your Text
Paste any text into the input box — customer reviews, social media comments, email feedback, or any prose. The analyzer works best on opinion-based text rather than purely factual or technical writing.
Step 2: Click Analyze
Click "Analyze Sentiment" to run the AFINN scoring. Each word is looked up in the lexicon. Positive words (like "excellent" at +3, "love" at +3) add to the score; negative words (like "terrible" at -3, "awful" at -3) subtract. The gauge shows where your text falls on the -5 to +5 scale.
Step 3: Review the Word Breakdown
The word scores table shows every matched word with its AFINN score and frequency. The highlighted text view colors positive words green and negative words red directly in your original text, making it easy to spot which phrases are driving the sentiment.
Understanding the Score Scale
The average score ranges from -5 (extremely negative) to +5 (extremely positive). A score of 0 is neutral. Scores from -1 to +1 are slightly negative or positive. Most natural writing falls between -2 and +2. Customer reviews for a 5-star product typically score between +1.5 and +3.
FAQ
Is the sentiment analyzer free to use?
Yes, completely free with no signup required. The entire analysis runs in your browser using the AFINN-165 lexicon — no text is sent to any server. You can analyze as much text as you like with no limits.
What is the AFINN-165 lexicon?
AFINN-165 is a list of about 3,300 English words rated from -5 (very negative) to +5 (very positive), created by Finn Arup Nielsen. It's one of the most widely used lexicons for simple, fast sentiment analysis. Words like 'love' score +3 and words like 'terrible' score -3.
How is the overall sentiment score calculated?
Each recognized word in your text is looked up in the AFINN lexicon and assigned its score. All scores are summed for the total score. The average score divides the total by the number of scored words, giving a normalized value between -5 and +5 regardless of text length.
Why does my text show Neutral even though it seems positive?
The AFINN lexicon only scores words it recognizes. Generic text with many neutral words (like 'the', 'and', 'meeting') will appear neutral because those words have no sentiment value. Sentiment analysis works best on opinion text, reviews, social media posts, and feedback.
Is sentiment analysis accurate?
Lexicon-based sentiment analysis is fast and transparent but has limits. It doesn't understand context, sarcasm, or negations ('not happy' would still score positive for 'happy'). For production applications, machine learning models are more accurate. This tool is best used for quick checks and educational purposes.
What types of text work best for sentiment analysis?
Product reviews, customer feedback, social media posts, emails, and opinion articles work best. Sentiment analysis is less reliable for technical writing, factual reports, or creative fiction where emotional language is used metaphorically.