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Text emotion recognition

In the face of paddlepaddle, natural language processing has also become very simple. To achieve text emotion recognition, we also need to install PaddlePaddle and Paddlehub. For specific installation, please see part 3.

And then there's the part of our code:

Import paddlehub as hub senta = hub.Module (name='senta_lstm') # load model sentence = [# prepare the sentences to be identified: 'you are so beautiful', 'you are so ugly','I'm so sad','I'm not happy', 'have a good time in this game', 'what junk game' ] results = senta.sentiment_classify (data= {text:sentence}) # emotion recognition # output recognition result for result in results: print (result)

The result of recognition is a list of dictionaries:

{'text':' you are beautiful, 'sentiment_label': 1,' sentiment_key': 'positive',' positive_probs': 0.9602, 'negative_probs': 0.0398}

{'text':' you are ugly, 'sentiment_label': 0,' sentiment_key': 'negative',' positive_probs': 0.0033, 'negative_probs': 0.9967}

{'text':' I'm so sad, 'sentiment_label': 1,' sentiment_key': 'positive',' positive_probs': 0.5324, 'negative_probs': 0.4676}

{'text':' I am unhappy', 'sentiment_label': 0,' sentiment_key': 'negative',' positive_probs': 0.1936, 'negative_probs': 0.8064}

{'text':' is a good game', 'sentiment_label': 1,' sentiment_key': 'positive',' positive_probs': 0.9933, 'negative_probs': 0.0067}

{'text':' what junk games', 'sentiment_label': 0,' sentiment_key': 'negative',' positive_probs': 0.0108, 'negative_probs': 0.9892}

The sentiment_key field contains emotional information.

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