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Definition of the Problem
Definition of the Problem

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    Do You Trust in Aspect-Based Sentiment Analysis?

    Definition of the Problem

    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.