The aim is to classify the sentiments of a text concerning given aspects. We have made several assumptions to make the service more helpful. Namely, the text being processed might be a full-length document, the aspects could contain several words (so may be defined more precisely), and most importantly, the service should provide an approximate explanation of any decision made, therefore, a user will be able to immediately infer the reliability of a prediction.
import aspect_based_sentiment_analysis as absa nlp = absa.load() text = ("We are great fans of Slack, but we wish the subscriptions " "were more accessible to small startups.") slack, price = nlp(text, aspects=['slack computer program', 'price']) assert price.sentiment == absa.Sentiment.negative assert slack.sentiment == absa.Sentiment.positive
Above is an example of how quickly you can start to benefit from our open-source package. All you need to do is to call the load function which sets up the ready-to-use pipeline nlp. You can explicitly pass the model name you wish to use (a list of available models is here), or a path to your model. In spite of the simplicity of using fine-tune models, we encourage you to build a custom model which reflects your data. The predictions will be more accurate and stable, something which we will discuss later on.