Speech Tone Analyzer
Paste a political speech excerpt and get instant analysis of its tone, readability, and emotional language. See five metrics visualized as horizontal bars with a written summary.
How Speech Tone Analysis Works
Five linguistic metrics combine to paint a complete picture of a speech's tone.
Sentence Length
Average words per sentence. Short sentences (12-18 words) feel urgent and direct. Long sentences (25+) feel formal and measured.
Sentiment Score
Ratio of positive to negative words. Scores above 0.5 lean positive, below -0.5 lean negative. Neutral speeches score near zero.
Emotional Language
Count of emotionally charged words per sentence. High emotional density signals a rallying or urgent tone. Low density suggests a policy-focused speech.
Frequently Asked Questions
Common questions about the speech tone analyzer.
The sentiment analysis uses a dictionary-based approach comparing positive and negative word counts. It is reasonably accurate for general tone detection but may miss nuanced sarcasm, irony, or culturally specific phrasing. It is designed for broad directional insight, not precise measurement.
The Flesch Reading Ease score measures how difficult a text is to read based on sentence length and syllable count. Scores range from 0 (very difficult, academic) to 100 (very easy, simple English). Political speeches typically score between 30 and 60, which corresponds to "college-level" reading difficulty.
Currently the tool is optimized for English text. The sentiment dictionary (AFINN-165) and Flesch formula are English-specific. Sentence length analysis will still work for other languages, but sentiment and reading ease results may be unreliable.
Emotional language includes words with strong affective connotations such as hope, fear, anger, love, betrayal, unity, and freedom. The tool compares text against a curated list of emotionally charged terms. Repetition of these words amplifies the emotional intensity score.
Yes. The Flesch Reading Ease formula is a well-established readability metric developed by Rudolf Flesch in 1948. Sentiment analysis uses the AFINN-165 word list by Finn Γ rup Nielsen. Passive voice detection follows standard English grammar rules. The tool applies established computational linguistics techniques.