def METHOD_NAME(self):
    # sentiment datase
    df_train = (
        self.load_sentiment_dl_dataset()
    )  #'/home/loan/Documents/freelancework/jsl/nlu/4realnlugit/tests/datasets/sentiment_dl/AllProductReviews.csv'
    print(df_train.columns)
    # convert int to str labels so our model predicts strings not numbers
    # the text data to use for classification should be in a column named 'text'
    df_train["text"] = df_train["text_data"]
    # the label column must have name 'y' name be of type str
    df_train["y"] = df_train["Sentiment"].astype(str)
    df_train.y = df_train.y.astype(str)
    df_train.y = df_train.y.str.replace("-1", "negative")
    df_train.y = df_train.y.str.replace("1", "positive")
    df_train = df_train.iloc[0:100]
    pipe = nlu.load("train.sentiment", verbose=True)
    pipe = pipe.fit(df_train)
    df = pipe.predict(df_train)
    print(df)
    print(df.columns)
    print(df)
    print(df.columns)
    for c in df.columns:
        print(df[c])
    # print(df[['sentiment','sentiment_confidence']])
    # print(df.sentiment.value_counts())
    # print(df.sentiment_confidence.value_counts())