Sentimental Classification for Multiple Sentences

[KaiyeYang-git]

I have understood how to classify one sentence's sentiments from '''TextClassifier.load('sentiment-fast')''' However, how can I do multiple sentences for their separate sentiments for having a fast processing speed? I know a for loop is functional for this case it is sooo slow

the example I am doing now is still based on one sentence: scaler = MinMaxScaler(feature_range=(-1, 1)) flair_score=[] def flairSentimentAnalysis(sentiment_text): classifier = TextClassifier.load('sentiment-fast') sentence = Sentence(sentiment_text) classifier.predict(sentence) sentence.labels[0].to_dict() attitude=sentence.labels[0].value score=sentence.labels[0].score if attitude=='NEGATIVE': score=-score return score

for text in raw_data['new_processed_text'][0:10]: score=flairSentimentAnalysis(text) flair_score.append(score) flair_attitude_score=np.array(flair_score).reshape(-1,1) flair_result=scaler.fit_transform(flair_attitude_score).flatten()

[KaiyeYang-git]

I have put all my sentences in a list and it can only generate one result which represents the sentiments of all sentence, but not the sentiments of each sentence.

[stale[bot]]

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.