Spaces:
No application file
No application file
| **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*. |