Text Sentiment Analyzer
Free Text Sentiment Analyzer for ai & predictive tools. Free online tool with accurate results using verified formulas.
Reviewed by Daniel Agrici, Founder & Lead Developer
Formula
Score = sum(word_score * negation * intensifier) / (N * max_score)
Each word is scored using a sentiment lexicon with values from -3 (most negative) to +3 (most positive). Scores are modified by negation (reversal) and intensifier multipliers. The normalized score divides the total by the maximum possible score, yielding a value from -1 to +1.
Worked Examples
Example 1: Positive Product Review
Problem:Analyze sentiment: 'I absolutely love this product! The quality is amazing and the customer service was incredibly helpful. Highly recommended!'
Solution:Positive words detected: love (+3), amazing (+3), helpful (+2), recommended (+2)\nIntensifiers: absolutely (2x on love = +6), incredibly (2x on helpful = +4), highly (1x on recommended)\nNegative words: none\nTotal positive score: 6 + 3 + 4 + 2 = 15\nNormalized score: 15/(4*3) = +1.0 (capped)\nSubjectivity: 4 sentiment words / 18 total = 22.2%
Result:Sentiment: Very Positive (+1.0) | Positive words: 4 | Intensity: High | Subjectivity: 22.2%
Example 2: Mixed Review Analysis
Problem:Analyze: 'The food was great but the service was terrible. I enjoyed the dessert although the prices seemed a bit expensive.'
Solution:Positive words: great (+2), enjoyed (+2)\nNegative words: terrible (-3), but (-0.5), although (-0.5), expensive (-1)\nIntensifiers: none significant\nTotal score: 2 + 2 - 3 - 0.5 - 0.5 - 1 = -1.0\nNormalized score: -1.0/(6*3) = -0.056\nSentiment: Slightly Negative\nSubjectivity: 6/21 = 28.6%
Result:Sentiment: Slightly Negative (-0.056) | Mixed: 2 positive, 4 negative | Subjectivity: 28.6%
Frequently Asked Questions
How does this sentiment analyzer handle negation and context?
This analyzer implements negation detection by scanning the three words preceding each sentiment word for negation markers like not, never, no, and contractions such as do not and is not. When a negation is detected, the sentiment score is reversed and slightly reduced (multiplied by -0.75) because negated positives are not as strongly negative as inherently negative words. For example, the phrase 'not good' receives a score of approximately -1.5 instead of the full -2 that 'bad' would receive. Intensifiers like very, extremely, and incredibly modify the magnitude of nearby sentiment words by a scaling factor. This contextual processing significantly improves accuracy compared to simple word counting approaches.
What is the difference between sentiment polarity and subjectivity?
Sentiment polarity measures the positive or negative orientation of text on a scale from -1 (most negative) to +1 (most positive). It answers the question of whether the opinion expressed is favorable or unfavorable. Subjectivity, on the other hand, measures how opinionated versus factual the text is, expressed as a percentage. A highly subjective text contains many opinion words, evaluative language, and personal judgments. A highly objective text presents facts, data, and neutral descriptions. A news article reporting election results would have low subjectivity, while an editorial about the same topic would have high subjectivity. Both dimensions are important for understanding text because a highly positive statement matters more when it is also highly subjective.
What are common applications of sentiment analysis in business?
Businesses use sentiment analysis across numerous applications. Brand monitoring tracks public sentiment about products and companies across social media, review sites, and news outlets. Customer feedback analysis automatically categorizes support tickets, reviews, and survey responses by sentiment to prioritize responses and identify trends. Market research analyzes consumer opinions about products, features, and competitors at scale. Financial markets use news sentiment analysis to predict stock price movements based on article tone. Political campaigns monitor voter sentiment on issues and candidates. Product development teams analyze feature request sentiment to prioritize roadmaps. Employee satisfaction surveys use sentiment analysis to identify morale issues and workplace concerns across large organizations.
How do intensifiers and modifiers affect sentiment scores?
Intensifiers and modifiers significantly alter the strength of sentiment expressions. Amplifying intensifiers like very, extremely, and incredibly multiply the base sentiment score by factors of 1.5 to 2.0, making positive words more positive and negative words more negative. Downtoning modifiers like somewhat, slightly, and barely reduce the sentiment intensity by multiplying scores by 0.5 to 0.75. For example, 'good' might score +2, while 'very good' scores +3 and 'slightly good' scores +1. Some systems also handle degree adverbs like 'too' which can flip sentiment entirely: 'too sweet' is negative despite 'sweet' being positive. Proper handling of intensifiers is crucial because they are among the most common sentiment modifiers in natural language.
References
Reviewed by Daniel Agrici, Founder & Lead Developer · Editorial policy