curl --request POST \
--url https://app.spotzee.com/api/ext/generic/text/sentiment-score \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/x-www-form-urlencoded' \
--data 'text=This is a sample text to analyze for readability and sentiment scores.'{
"status": "success",
"data": {
"score": 3,
"comparative": 0.25,
"calculation": [],
"tokens": [
"i",
"love",
"this",
"awesome",
"great",
"product"
],
"words": [
"love",
"awesome",
"great"
],
"positive": [
"love",
"awesome",
"great"
],
"negative": []
}
}Performs sentiment analysis on text using advanced NLP techniques. Returns detailed sentiment scores (positive, negative, neutral) along with confidence levels and key phrases that influenced the analysis. Useful for analyzing customer feedback, reviews, and social media content.
curl --request POST \
--url https://app.spotzee.com/api/ext/generic/text/sentiment-score \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/x-www-form-urlencoded' \
--data 'text=This is a sample text to analyze for readability and sentiment scores.'{
"status": "success",
"data": {
"score": 3,
"comparative": 0.25,
"calculation": [],
"tokens": [
"i",
"love",
"this",
"awesome",
"great",
"product"
],
"words": [
"love",
"awesome",
"great"
],
"positive": [
"love",
"awesome",
"great"
],
"negative": []
}
}Bearer token authentication. Use format: Bearer YOUR_API_KEY
Text content to analyze
"This is a sample text to analyze for readability and sentiment scores."
Successfully analyzed sentiment
"success"
Hide child attributes
Overall sentiment score. Negative values indicate negative sentiment, positive values indicate positive sentiment, 0 is neutral.
3
Normalized sentiment score per token. Useful for comparing texts of different lengths.
0.25
All tokens (words) analyzed in the text.
[
"i",
"love",
"this",
"awesome",
"great",
"product"
]
All sentiment-bearing words found in the text.
["love", "awesome", "great"]
Positive sentiment words found in the text.
["love", "awesome", "great"]
Negative sentiment words found in the text.
[]
{
"score": 3,
"comparative": 0.25,
"calculation": [],
"tokens": [
"i",
"love",
"this",
"awesome",
"great",
"product"
],
"words": ["love", "awesome", "great"],
"positive": ["love", "awesome", "great"],
"negative": []
}
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