Entity and Sentiment Analysis with the Natural Language API
The Cloud Natural Language API lets you extract entities from text, perform sentiment and syntactic analysis, and classify text into categories.
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Creating a Natural Language API request and calling the API with curl
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Extracting entities and running sentiment analysis on text with the Natural Language API
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Performing linguistic analysis on text with the Natural Language API
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Creating a Natural Language API request in a different language
1. Create an API key
2. Make an entity analysis request
student-04-5e2e787da110@linux-instance:~$ cat request.json
{
"document":{
"type":"PLAIN_TEXT",
"content":"Joanne Rowling, who writes under the pen names J. K. Rowling and Robert Galbraith, is a British novelist and screenwriter who wrote the Harry Potter fantasy series."
},
"encodingType":"UTF8"
}
student-04-5e2e787da110@linux-instance:~$
3. Call the Natural Language API
student-04-5e2e787da110@linux-instance:~$ curl "https://language.googleapis.com/v1/documents:analyzeEntities?key=${API_KEY}" \
-s -X POST -H "Content-Type: application/json" --data-binary @request.json > result.json
student-04-5e2e787da110@linux-instance:~$ cat result.json
{
"entities": [
{
"name": "Joanne Rowling",
"type": "PERSON",
"metadata": {
"wikipedia_url": "https://en.wikipedia.org/wiki/J._K._Rowling",
"mid": "/m/042xh"
},
"salience": 0.79828626,
"mentions": [
{
"text": {
"content": "Joanne Rowling",
"beginOffset": 0
},
"type": "PROPER"
},
{
"text": {
"content": "Rowling",
"beginOffset": 53
},
"type": "PROPER"
},
{
"text": {
"content": "novelist",
"beginOffset": 96
},
"type": "COMMON"
},
{
"text": {
"content": "Robert Galbraith",
"beginOffset": 65
},
"type": "PROPER"
}
]
},
{
"name": "pen names",
"type": "OTHER",
"metadata": {},
"salience": 0.07300248,
"mentions": [
{
"text": {
"content": "pen names",
"beginOffset": 37
},
"type": "COMMON"
}
]
},
{
"name": "J.K.",
"type": "PERSON",
"metadata": {},
"salience": 0.043804582,
"mentions": [
{
"text": {
"content": "J. K.",
"beginOffset": 47
},
"type": "PROPER"
}
]
},
{
"name": "British",
"type": "LOCATION",
"metadata": {
"mid": "/m/07ssc",
"wikipedia_url": "https://en.wikipedia.org/wiki/United_Kingdom"
},
"salience": 0.019752095,
"mentions": [
{
"text": {
"content": "British",
"beginOffset": 88
},
"type": "PROPER"
}
]
},
{
"name": "fantasy series",
"type": "WORK_OF_ART",
"metadata": {},
"salience": 0.01764168,
"mentions": [
{
"text": {
"content": "fantasy series",
"beginOffset": 149
},
"type": "COMMON"
}
]
},
{
"name": "Harry Potter",
"type": "WORK_OF_ART",
"metadata": {
"mid": "/m/078ffw",
"wikipedia_url": "https://en.wikipedia.org/wiki/Harry_Potter"
},
"salience": 0.014916742,
"mentions": [
{
"text": {
"content": "Harry Potter",
"beginOffset": 136
},
"type": "PROPER"
}
]
},
{
"name": "screenwriter",
"type": "PERSON",
"metadata": {},
"salience": 0.011085264,
"mentions": [
{
"text": {
"content": "screenwriter",
"beginOffset": 109
},
"type": "COMMON"
}
]
}
],
"language": "en"
}
student-04-5e2e787da110@linux-instance:~$
4. Sentiment analysis with the Natural Language API
student-04-5e2e787da110@linux-instance:~$ cat request.json
{
"document":{
"type":"PLAIN_TEXT",
"content":"Harry Potter is the best book. I think everyone should read it."
},
"encodingType": "UTF8"
}
student-04-5e2e787da110@linux-instance:~$ curl "https://language.googleapis.com/v1/documents:analyzeSentiment?key=${API_KEY}" \
-s -X POST -H "Content-Type: application/json" --data-binary @request.json
{
"documentSentiment": {
"magnitude": 1.9,
"score": 0.9
},
"language": "en",
"sentences": [
{
"text": {
"content": "Harry Potter is the best book.",
"beginOffset": 0
},
"sentiment": {
"magnitude": 0.9,
"score": 0.9
}
},
{
"text": {
"content": "I think everyone should read it.",
"beginOffset": 31
},
"sentiment": {
"magnitude": 0.9,
"score": 0.9
}
}
]
}
student-04-5e2e787da110@linux-instance:~$
5. Analyzing entity sentiment
student-04-5e2e787da110@linux-instance:~$ cat request.json
{
"document":{
"type":"PLAIN_TEXT",
"content":"I liked the sushi but the service was terrible."
},
"encodingType": "UTF8"
}
student-04-5e2e787da110@linux-instance:~$ curl "https://language.googleapis.com/v1/documents:analyzeEntitySentiment?key=${API_KEY}" \
-s -X POST -H "Content-Type: application/json" --data-binary @request.json
{
"entities": [
{
"name": "sushi",
"type": "CONSUMER_GOOD",
"metadata": {},
"salience": 0.51064336,
"mentions": [
{
"text": {
"content": "sushi",
"beginOffset": 12
},
"type": "COMMON",
"sentiment": {
"magnitude": 0,
"score": 0
}
}
],
"sentiment": {
"magnitude": 0,
"score": 0
}
},
{
"name": "service",
"type": "OTHER",
"metadata": {},
"salience": 0.48935664,
"mentions": [
{
"text": {
"content": "service",
"beginOffset": 26
},
"type": "COMMON",
"sentiment": {
"magnitude": 0.7,
"score": -0.7
}
}
],
"sentiment": {
"magnitude": 0.7,
"score": -0.7
}
}
],
"language": "en"
}
student-04-5e2e787da110@linux-instance:~$
6. Analyzing syntax and parts of speech
student-04-5e2e787da110@linux-instance:~$ cat request.json
{
"document":{
"type":"PLAIN_TEXT",
"content": "Joanne Rowling is a British novelist, screenwriter and film producer."
},
"encodingType": "UTF8"
}
student-04-5e2e787da110@linux-instance:~$ curl "https://language.googleapis.com/v1/documents:analyzeSyntax?key=${API_KEY}" \
-s -X POST -H "Content-Type: application/json" --data-binary @request.json
{
"sentences": [
{
"text": {
"content": "Joanne Rowling is a British novelist, screenwriter and film producer.",
"beginOffset": 0
}
}
],
"tokens": [
{
"text": {
"content": "Joanne",
"beginOffset": 0
},
"partOfSpeech": {
"tag": "NOUN",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "SINGULAR",
"person": "PERSON_UNKNOWN",
"proper": "PROPER",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 1,
"label": "NN"
},
"lemma": "Joanne"
},
{
"text": {
"content": "Rowling",
"beginOffset": 7
},
"partOfSpeech": {
"tag": "NOUN",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "SINGULAR",
"person": "PERSON_UNKNOWN",
"proper": "PROPER",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 2,
"label": "NSUBJ"
},
"lemma": "Rowling"
},
{
"text": {
"content": "is",
"beginOffset": 15
},
"partOfSpeech": {
"tag": "VERB",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "INDICATIVE",
"number": "SINGULAR",
"person": "THIRD",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "PRESENT",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 2,
"label": "ROOT"
},
"lemma": "be"
},
{
"text": {
"content": "a",
"beginOffset": 18
},
"partOfSpeech": {
"tag": "DET",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "NUMBER_UNKNOWN",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 5,
"label": "DET"
},
"lemma": "a"
},
{
"text": {
"content": "British",
"beginOffset": 20
},
"partOfSpeech": {
"tag": "ADJ",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "NUMBER_UNKNOWN",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 5,
"label": "AMOD"
},
"lemma": "British"
},
{
"text": {
"content": "novelist",
"beginOffset": 28
},
"partOfSpeech": {
"tag": "NOUN",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "SINGULAR",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 2,
"label": "ATTR"
},
"lemma": "novelist"
},
{
"text": {
"content": ",",
"beginOffset": 36
},
"partOfSpeech": {
"tag": "PUNCT",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "NUMBER_UNKNOWN",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 5,
"label": "P"
},
"lemma": ","
},
{
"text": {
"content": "screenwriter",
"beginOffset": 38
},
"partOfSpeech": {
"tag": "NOUN",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "SINGULAR",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 5,
"label": "CONJ"
},
"lemma": "screenwriter"
},
{
"text": {
"content": "and",
"beginOffset": 51
},
"partOfSpeech": {
"tag": "CONJ",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "NUMBER_UNKNOWN",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 5,
"label": "CC"
},
"lemma": "and"
},
{
"text": {
"content": "film",
"beginOffset": 55
},
"partOfSpeech": {
"tag": "NOUN",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "SINGULAR",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 10,
"label": "NN"
},
"lemma": "film"
},
{
"text": {
"content": "producer",
"beginOffset": 60
},
"partOfSpeech": {
"tag": "NOUN",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "SINGULAR",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 5,
"label": "CONJ"
},
"lemma": "producer"
},
{
"text": {
"content": ".",
"beginOffset": 68
},
"partOfSpeech": {
"tag": "PUNCT",
"aspect": "ASPECT_UNKNOWN",
"case": "CASE_UNKNOWN",
"form": "FORM_UNKNOWN",
"gender": "GENDER_UNKNOWN",
"mood": "MOOD_UNKNOWN",
"number": "NUMBER_UNKNOWN",
"person": "PERSON_UNKNOWN",
"proper": "PROPER_UNKNOWN",
"reciprocity": "RECIPROCITY_UNKNOWN",
"tense": "TENSE_UNKNOWN",
"voice": "VOICE_UNKNOWN"
},
"dependencyEdge": {
"headTokenIndex": 2,
"label": "P"
},
"lemma": "."
}
],
"language": "en"
}
student-04-5e2e787da110@linux-instance:~$
7. Multilingual natural language processing
student-04-5e2e787da110@linux-instance:~$ cat request.json
{
"document":{
"type":"PLAIN_TEXT",
"content":"日本のグーグルのオフィスは、東京の六本木ヒルズにあります"
}
}
student-04-5e2e787da110@linux-instance:~$ curl "https://language.googleapis.com/v1/documents:analyzeEntities?key=${API_KEY}" \
-s -X POST -H "Content-Type: application/json" --data-binary @request.json
{
"entities": [
{
"name": "日本",
"type": "LOCATION",
"metadata": {
"wikipedia_url": "https://en.wikipedia.org/wiki/Japan",
"mid": "/m/03_3d"
},
"salience": 0.23804513,
"mentions": [
{
"text": {
"content": "日本",
"beginOffset": -1
},
"type": "PROPER"
}
]
},
{
"name": "グーグル",
"type": "ORGANIZATION",
"metadata": {
"wikipedia_url": "https://en.wikipedia.org/wiki/Google",
"mid": "/m/045c7b"
},
"salience": 0.21214141,
"mentions": [
{
"text": {
"content": "グーグル",
"beginOffset": -1
},
"type": "PROPER"
}
]
},
{
"name": "六本木ヒルズ",
"type": "PERSON",
"metadata": {
"wikipedia_url": "https://en.wikipedia.org/wiki/Roppongi_Hills",
"mid": "/m/01r2_k"
},
"salience": 0.19418614,
"mentions": [
{
"text": {
"content": "六本木ヒルズ",
"beginOffset": -1
},
"type": "PROPER"
}
]
},
{
"name": "東京",
"type": "LOCATION",
"metadata": {
"wikipedia_url": "https://de.wikipedia.org/wiki/Tokio",
"mid": "/g/12lnhn10f"
},
"salience": 0.18159479,
"mentions": [
{
"text": {
"content": "東京",
"beginOffset": -1
},
"type": "PROPER"
}
]
},
{
"name": "オフィス",
"type": "OTHER",
"metadata": {},
"salience": 0.17403255,
"mentions": [
{
"text": {
"content": "オフィス",
"beginOffset": -1
},
"type": "COMMON"
}
]
}
],
"language": "ja"
}
student-04-5e2e787da110@linux-instance:~$
We’ve learned how to perform text analysis with the Cloud Natural Language API by extracting entities, analyzing sentiment, and doing syntax annotation.
