Path: blob/develop/awscli/examples/comprehend/batch-detect-targeted-sentiment.rst
1567 views
**To detect the sentiment and each named entity for multiple input texts**
The following ``batch-detect-targeted-sentiment`` example analyzes multiple input texts and returns the named entities along with the prevailing sentiment attached to each entity. The pre-trained model's confidence score is also output for each prediction. ::
aws comprehend batch-detect-targeted-sentiment \
--language-code en \
--text-list "That movie was really boring, the original was way more entertaining" "The trail is extra beautiful today." "My meal was just okay."
Output::
{
"ResultList": [
{
"Index": 0,
"Entities": [
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9999009966850281,
"GroupScore": 1.0,
"Text": "movie",
"Type": "MOVIE",
"MentionSentiment": {
"Sentiment": "NEGATIVE",
"SentimentScore": {
"Positive": 0.13887299597263336,
"Negative": 0.8057460188865662,
"Neutral": 0.05525200068950653,
"Mixed": 0.00012799999967683107
}
},
"BeginOffset": 5,
"EndOffset": 10
}
]
},
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9921110272407532,
"GroupScore": 1.0,
"Text": "original",
"Type": "MOVIE",
"MentionSentiment": {
"Sentiment": "POSITIVE",
"SentimentScore": {
"Positive": 0.9999989867210388,
"Negative": 9.999999974752427e-07,
"Neutral": 0.0,
"Mixed": 0.0
}
},
"BeginOffset": 34,
"EndOffset": 42
}
]
}
]
},
{
"Index": 1,
"Entities": [
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.7545599937438965,
"GroupScore": 1.0,
"Text": "trail",
"Type": "OTHER",
"MentionSentiment": {
"Sentiment": "POSITIVE",
"SentimentScore": {
"Positive": 1.0,
"Negative": 0.0,
"Neutral": 0.0,
"Mixed": 0.0
}
},
"BeginOffset": 4,
"EndOffset": 9
}
]
},
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9999960064888,
"GroupScore": 1.0,
"Text": "today",
"Type": "DATE",
"MentionSentiment": {
"Sentiment": "NEUTRAL",
"SentimentScore": {
"Positive": 9.000000318337698e-06,
"Negative": 1.9999999949504854e-06,
"Neutral": 0.9999859929084778,
"Mixed": 3.999999989900971e-06
}
},
"BeginOffset": 29,
"EndOffset": 34
}
]
}
]
},
{
"Index": 2,
"Entities": [
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9999880194664001,
"GroupScore": 1.0,
"Text": "My",
"Type": "PERSON",
"MentionSentiment": {
"Sentiment": "NEUTRAL",
"SentimentScore": {
"Positive": 0.0,
"Negative": 0.0,
"Neutral": 1.0,
"Mixed": 0.0
}
},
"BeginOffset": 0,
"EndOffset": 2
}
]
},
{
"DescriptiveMentionIndex": [
0
],
"Mentions": [
{
"Score": 0.9995260238647461,
"GroupScore": 1.0,
"Text": "meal",
"Type": "OTHER",
"MentionSentiment": {
"Sentiment": "NEUTRAL",
"SentimentScore": {
"Positive": 0.04695599898695946,
"Negative": 0.003226999891921878,
"Neutral": 0.6091709733009338,
"Mixed": 0.34064599871635437
}
},
"BeginOffset": 3,
"EndOffset": 7
}
]
}
]
}
],
"ErrorList": []
}
For more information, see `Targeted Sentiment <https://docs.aws.amazon.com/comprehend/latest/dg/how-targeted-sentiment.html>`__ in the *Amazon Comprehend Developer Guide*.