Languages： Chinese English French Korean Japanese Arabic Russian Spanish Portuguese German
The text sentiment analysis algorithm can automatically analyze and recognize the opinions or sentimental tendencies expressed in the text, and give the sentimental orientation indicators that can express the polarity and intensity of sentiments.
The sentiment analysis algorithm is applied for the analysis of sentimental polarity, bearing an indispensable significance in public opinion monitoring, topic supervision, word of mouth analysis and other fields. The sentiment analysis algorithm is based on the deep learning model and trained on the basis of 100,000-class manually annotated corpus, which can achieve an accuracy rate of 70% when five sentimental polarity indicators are applied including extremely positive, general positive, neutral, general negative, and extremely negative. Currently it supports 10 languages, including Chinese, English, Japanese, Korean, Russian, Portuguese, Spanish, French, Germany and Arabic.