0
# Text Analysis
1

2
Multi-sentence text processing with comprehensive entity extraction, pattern matching, and document-level transformations. The Text class is designed for analyzing documents, paragraphs, or any text containing multiple sentences.
3

4
## Capabilities
5

6
### Text Class
7

8
Container for multiple sentences with document-level operations and entity extraction.
9

10
```javascript { .api }
11
/**
12
 * Text class for multi-sentence analysis and transformation
13
 */
14
class Text {
15
  /** Array of parsed sentences */
16
  sentences: Sentence[];
17
  /** Original input text */
18
  raw_text: string;
19
  /** Contraction expansion/contraction methods */
20
  contractions: Contractions;
21
}
22
```
23

24
### Text Output Methods
25

26
Methods for retrieving processed text in different formats.
27

28
```javascript { .api }
29
/**
30
 * Get formatted text with original spacing and punctuation
31
 * @returns {string} Reconstructed text
32
 */
33
text(): string;
34

35
/**
36
 * Get normalized text with cleaned whitespace
37
 * @returns {string} Normalized text
38
 */
39
normal(): string;
40

41
/**
42
 * Get root/lemmatized form of all words
43
 * @returns {string} Root form text
44
 */
45
root(): string;
46
```
47

48
**Usage Examples:**
49

50
```javascript
51
const nlp = require('nlp_compromise');
52

53
const doc = nlp.text('She walks quickly. He ran slowly.');
54

55
console.log(doc.text());   // 'She walks quickly. He ran slowly.'
56
console.log(doc.normal()); // 'she walks quickly he ran slowly'
57
console.log(doc.root());   // 'she walk quickly he run slowly'
58
```
59

60
### Term and Tag Access
61

62
Methods for accessing individual terms and their grammatical tags.
63

64
```javascript { .api }
65
/**
66
 * Get all terms from all sentences
67
 * @returns {Term[]} Array of all terms
68
 */
69
terms(): Term[];
70

71
/**
72
 * Get part-of-speech tags for all sentences
73
 * @returns {string[][]} Array of tag arrays for each sentence
74
 */
75
tags(): string[][];
76
```
77

78
**Usage Examples:**
79

80
```javascript
81
const doc = nlp.text('Dogs bark loudly.');
82
const terms = doc.terms();
83
console.log(terms[0].text); // 'Dogs'
84
console.log(terms[0].tag);  // 'Plural'
85

86
const tags = doc.tags();
87
console.log(tags[0]); // ['Plural', 'Verb', 'Adverb']
88
```
89

90
### Pattern Matching
91

92
Advanced pattern matching across the entire text with support for grammatical patterns.
93

94
```javascript { .api }
95
/**
96
 * Find patterns across all sentences in the text
97
 * @param {string} pattern - Search pattern (supports grammatical patterns)
98
 * @param {object} [options] - Matching options
99
 * @returns {Result[]} Array of matching results
100
 */
101
match(pattern, options): Result[];
102

103
/**
104
 * Replace patterns across all sentences
105
 * @param {string} pattern - Pattern to replace
106
 * @param {string} replacement - Replacement text
107
 * @param {object} [options] - Replacement options
108
 * @returns {Text} Modified text object
109
 */
110
replace(pattern, replacement, options): Text;
111
```
112

113
**Usage Examples:**
114

115
```javascript
116
const doc = nlp.text('The big dog barked. A small cat meowed.');
117

118
// Find adjective-noun patterns
119
const matches = doc.match('[Adjective] [Noun]');
120
console.log(matches[0].text()); // 'big dog'
121
console.log(matches[1].text()); // 'small cat'
122

123
// Replace patterns
124
doc.replace('[Adjective] dog', 'tiny puppy');
125
console.log(doc.text()); // 'The tiny puppy barked. A small cat meowed.'
126
```
127

128
### Grammatical Transformations
129

130
Document-level transformations that apply to all verbs in the text.
131

132
```javascript { .api }
133
/**
134
 * Convert all verbs to past tense
135
 * @returns {Text} Text with past tense verbs
136
 */
137
to_past(): Text;
138

139
/**
140
 * Convert all verbs to present tense
141
 * @returns {Text} Text with present tense verbs
142
 */
143
to_present(): Text;
144

145
/**
146
 * Convert all verbs to future tense
147
 * @returns {Text} Text with future tense verbs
148
 */
149
to_future(): Text;
150

151
/**
152
 * Negate all sentences in the text
153
 * @returns {Text} Text with negated sentences
154
 */
155
negate(): Text;
156
```
157

158
**Usage Examples:**
159

160
```javascript
161
const doc = nlp.text('She walks to school. He runs home.');
162

163
console.log(doc.to_past().text());    // 'She walked to school. He ran home.'
164
console.log(doc.to_future().text());  // 'She will walk to school. He will run home.'
165
console.log(doc.negate().text());     // 'She doesn\'t walk to school. He doesn\'t run home.'
166
```
167

168
### Entity Extraction
169

170
Comprehensive entity recognition and extraction across the entire document.
171

172
```javascript { .api }
173
/**
174
 * Extract all person entities from text
175
 * @returns {Person[]} Array of Person objects
176
 */
177
people(): Person[];
178

179
/**
180
 * Extract all place entities from text
181
 * @returns {Place[]} Array of Place objects
182
 */
183
places(): Place[];
184

185
/**
186
 * Extract all organization entities from text
187
 * @returns {Organization[]} Array of Organization objects
188
 */
189
organizations(): Organization[];
190

191
/**
192
 * Extract all date entities from text
193
 * @returns {Date[]} Array of Date objects
194
 */
195
dates(): Date[];
196

197
/**
198
 * Extract all numeric values and measurements
199
 * @returns {Value[]} Array of Value objects
200
 */
201
values(): Value[];
202

203
/**
204
 * Extract topics and keywords with frequency counts
205
 * @returns {Topic[]} Array of Topic objects with counts
206
 */
207
topics(): Topic[];
208
```
209

210
**Usage Examples:**
211

212
```javascript
213
const doc = nlp.text(`
214
  Tony Hawk visited Apple Inc. in Cupertino on March 15th.
215
  He spent $500 on new skateboard equipment.
216
`);
217

218
const people = doc.people();
219
console.log(people[0].text);        // 'Tony Hawk'
220
console.log(people[0].firstName);   // 'Tony'
221

222
const places = doc.places();
223
console.log(places[0].text);        // 'Cupertino'
224

225
const orgs = doc.organizations();
226
console.log(orgs[0].text);          // 'Apple Inc.'
227

228
const dates = doc.dates();  
229
console.log(dates[0].text);         // 'March 15th'
230

231
const values = doc.values();
232
console.log(values[0].number);      // 500
233
console.log(values[0].unit);        // 'dollar'
234
```
235

236
### Part-of-Speech Extraction
237

238
Extract all words of specific grammatical categories from the text.
239

240
```javascript { .api }
241
/**
242
 * Extract all nouns from text
243
 * @returns {Noun[]} Array of Noun objects
244
 */
245
nouns(): Noun[];
246

247
/**
248
 * Extract all adjectives from text  
249
 * @returns {Adjective[]} Array of Adjective objects
250
 */
251
adjectives(): Adjective[];
252

253
/**
254
 * Extract all verbs from text
255
 * @returns {Verb[]} Array of Verb objects
256
 */
257
verbs(): Verb[];
258

259
/**
260
 * Extract all adverbs from text
261
 * @returns {Adverb[]} Array of Adverb objects
262
 */
263
adverbs(): Adverb[];
264
```
265

266
**Usage Examples:**
267

268
```javascript
269
const doc = nlp.text('The quick brown fox jumps quickly over the lazy dog.');
270

271
const nouns = doc.nouns();
272
console.log(nouns.map(n => n.text)); // ['fox', 'dog']
273

274
const adjectives = doc.adjectives();
275
console.log(adjectives.map(a => a.text)); // ['quick', 'brown', 'lazy']
276

277
const verbs = doc.verbs();
278
console.log(verbs.map(v => v.text)); // ['jumps']
279

280
const adverbs = doc.adverbs();
281
console.log(adverbs.map(a => a.text)); // ['quickly']
282
```
283

284
### Contractions
285

286
Methods for expanding and contracting text-wide contractions.
287

288
```javascript { .api }
289
/**
290
 * Contractions object for expansion/contraction operations
291
 */
292
interface Contractions {
293
  /**
294
   * Expand all contractions in text (he'd → he would)
295
   * @returns {Text} Text with expanded contractions
296
   */
297
  expand(): Text;
298
  
299
  /**
300
   * Contract appropriate phrases (he would → he'd)
301
   * @returns {Text} Text with contracted phrases
302
   */
303
  contract(): Text;
304
}
305
```
306

307
**Usage Examples:**
308

309
```javascript
310
const doc = nlp.text("He'd like to go. She's very smart.");
311

312
console.log(doc.contractions.expand().text());
313
// "He would like to go. She is very smart."
314

315
const expanded = nlp.text("He would like to go. She is very smart.");
316
console.log(expanded.contractions.contract().text());
317
// "He'd like to go. She's very smart."
318
```