Text Analysis & NLP

Analyze text with precision. From simple word counts to advanced NLP with sentiment analysis and entity extraction.

Word & Character Count

Accurate text statistics including word count, character count (with and without whitespace), line count, paragraph count, and sentence count. Handles Unicode text correctly, including CJK characters and emoji sequences.

Request json

1{
2  "request_id": "wc-001",
3  "type": "word_count",
4  "text": "The quick brown fox jumps over the lazy dog. It was a sunny day in the park."
5}

Response json

1{
2  "request_id": "wc-001",
3  "result": {
4    "words": 16,
5    "characters": 76,
6    "characters_no_spaces": 62,
7    "sentences": 2,
8    "paragraphs": 1,
9    "lines": 1
10  }
11}

Spell Check

Dictionary-based spell checking that identifies misspelled words and provides ranked correction suggestions. Supports custom dictionaries for domain-specific terminology. Returns word positions for precise error highlighting.

Request json

1{
2  "request_id": "spell-001",
3  "type": "spell_check",
4  "text": "The quik brown fox jumpd over the lazi dog.",
5  "language": "en"
6}

Response json

1{
2  "request_id": "spell-001",
3  "result": {
4    "errors": [
5      {
6        "word": "quik",
7        "position": 4,
8        "suggestions": ["quick", "quik", "quiz"]
9      },
10      {
11        "word": "jumpd",
12        "position": 20,
13        "suggestions": ["jumped", "jumps", "jump"]
14      },
15      {
16        "word": "lazi",
17        "position": 35,
18        "suggestions": ["lazy", "laze", "lazi"]
19      }
20    ],
21    "error_count": 3
22  }
23}

Readability Scoring

Compute multiple readability metrics to assess text complexity. Includes Flesch-Kincaid Grade Level, Flesch Reading Ease, Gunning Fog Index, Coleman-Liau Index, and Automated Readability Index. Useful for content optimization and accessibility compliance.

Request json

1{
2  "request_id": "read-001",
3  "type": "readability_score",
4  "text": "Machine learning models process input data through layers of neural networks to produce predictions."
5}

Response json

1{
2  "request_id": "read-001",
3  "result": {
4    "flesch_reading_ease": 32.5,
5    "flesch_kincaid_grade": 14.2,
6    "gunning_fog": 16.8,
7    "coleman_liau": 15.1,
8    "automated_readability": 15.6,
9    "grade_level": "College",
10    "avg_words_per_sentence": 15.0,
11    "avg_syllables_per_word": 2.1
12  }
13}

Sentiment Analysis

Classify text sentiment as positive, negative, or neutral with confidence scoring. The analysis considers word-level sentiment, negation handling, and intensifiers. Returns an overall sentiment label and a numeric score from -1.0 (most negative) to 1.0 (most positive).

Request json

1{
2  "request_id": "sent-001",
3  "type": "analyze_sentiment",
4  "text": "This product is absolutely fantastic! The quality exceeded my expectations and the customer service was wonderful."
5}

Response json

1{
2  "request_id": "sent-001",
3  "result": {
4    "sentiment": "positive",
5    "score": 0.92,
6    "confidence": 0.96,
7    "breakdown": {
8      "positive_words": ["fantastic", "exceeded", "wonderful"],
9      "negative_words": [],
10      "intensifiers": ["absolutely"]
11    }
12  }
13}

Entity Extraction

Named entity recognition that identifies and classifies entities in text. Detects persons, organizations, locations, dates, monetary amounts, and percentages. Each entity includes its type, the matched text span, and position within the source document.

Request json

1{
2  "request_id": "ner-001",
3  "type": "extract_entities",
4  "text": "Apple CEO Tim Cook announced a $50 billion investment in California on January 15, 2025."
5}

Response json

1{
2  "request_id": "ner-001",
3  "result": {
4    "entities": [
5      { "text": "Apple", "type": "ORGANIZATION", "start": 0, "end": 5 },
6      { "text": "Tim Cook", "type": "PERSON", "start": 10, "end": 18 },
7      { "text": "$50 billion", "type": "MONEY", "start": 31, "end": 42 },
8      { "text": "California", "type": "LOCATION", "start": 57, "end": 67 },
9      { "text": "January 15, 2025", "type": "DATE", "start": 71, "end": 87 }
10    ],
11    "entity_count": 5
12  }
13}

Language Detection

Automatic language identification from text input. Supports 50+ languages with confidence scoring. Returns the top detected language along with alternative candidates. Handles multilingual text and short passages where detection is more challenging.

Request json

1{
2  "request_id": "lang-001",
3  "type": "detect_language",
4  "text": "Le renard brun rapide saute par-dessus le chien paresseux."
5}

Response json

1{
2  "request_id": "lang-001",
3  "result": {
4    "language": "fr",
5    "language_name": "French",
6    "confidence": 0.98,
7    "alternatives": [
8      { "language": "it", "language_name": "Italian", "confidence": 0.01 },
9      { "language": "es", "language_name": "Spanish", "confidence": 0.005 }
10    ]
11  }
12}

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