sentiment.py 文件源码

def build_models_NLP(train_pos_vec, train_neg_vec):
    """
    Returns a BernoulliNB and LosticRegression Model that are fit to the training data.
    """
    Y = ["pos"]*len(train_pos_vec) + ["neg"]*len(train_neg_vec)

    # Use sklearn's BernoulliNB and LogisticRegression functions to fit two models to the training data.
    # For BernoulliNB, use alpha=1.0 and binarize=None
    # For LogisticRegression, pass no parameters
    train_vec = []
    train_vec.extend(train_pos_vec)
    train_vec.extend(train_neg_vec)

    nb_model = BernoulliNB(alpha=1.0, binarize=None, class_prior=None, fit_prior=True)
    nb_model.fit(train_vec, Y)

    lr_model = LogisticRegression()
    lr_model.fit(train_vec, Y)

    return nb_model, lr_model