Text Sentiment Detector / Tone Analyzer

Analyze the emotional tone of your writing with this privacy-focused Sentiment Detector. Uses the AFINN-165 lexicon to score text positivity without external APIs.

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Category Text Analysis
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About

This Text Sentiment Detector is a client-side analytical tool designed to evaluate the emotional tone of written content. Unlike cloud-based AI solutions that transmit your data to third-party servers, this tool processes everything locally within your browser using a weighted lexical database. It is particularly useful for copywriters, editors, and customer support managers who need to ensure their communication strikes the right balance between professional neutrality, enthusiastic positivity, or empathetic concern.

The tool operates by parsing input text against a predefined lexicon of over 3,000 words, each assigned a valence rating ranging from -5 (extremely negative) to +5 (extremely positive). This granular approach allows for the detection of subtle tonal shifts that binary 'happy/sad' classifiers often miss. It provides a raw cumulative score, a comparative density score, and a visual heatmap of the text to highlight specific emotional triggers.

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Formulas

The sentiment calculation relies on a summation of valence integers normalized against the text length to determine density.

Stotal = ∑ (wi × vi)
Scomp = Stotal / N

Where:

  • Stotal = Total Sentiment Score
  • w = Occurrences of the word
  • v = Valence rating (-5 to +5)
  • N = Total word count (excluding whitespace)
  • Scomp = Comparative Score (Intensity)

A Comparative Score usually ranges between -1.0 and 1.0. A score above 0.05 is generally considered positive, while a score below -0.05 is considered negative. Scores between those bounds represent neutral sentiment.

Reference Data

WordValence ScoreContextual Meaning
Breathless+5Extreme excitement or anticipation
Outstanding+5Superior quality or performance
Win+4Success, achievement
Glad+3Moderate happiness
Chance+2Opportunity, possibility
Fake-3Deception, lack of authenticity
Disaster-4Catastrophic failure
Catastrophic-5Extreme negative impact

Frequently Asked Questions

Lexicon-based analysis has limitations with sarcasm. Since the tool calculates sentiment based on individual word values (e.g., 'great' is +3), a phrase like 'Oh, great job breaking the plate' will register as positive due to the word 'great'. Contextual nuance requires deep learning models, whereas this tool focuses on explicit lexical valence.
This tool uses a deterministic dictionary approach (AFINN-165). It adds up fixed numbers associated with words. LLMs (Large Language Models) understand context, syntax, and semantic relationships. This tool provides a mathematical baseline for 'emotional keyword density' rather than a semantic understanding of intent.
Yes. A long text with many slightly positive words will have a high Total Score. To understand the 'intensity' of the emotion regardless of length, look at the Comparative Score, which divides the total by the number of words.