#TSNE

    from sklearn.manifold import TSNE

    labels = final['Score']


    model = TSNE(n_components=2, random_state=0, perplexity =10, n_iter = 1000)
   # configuring the parameters
  # the number of components = 2
    # default perplexity = 30
    # default learning rate = 200
    # default Maximum number of iterations for the optimization = 1000


    tsne_data = model.fit_transform(tfidf_sent_vectors)


    # creating a new data frame which help us in plotting the result data
    tsne_data = np.vstack((tsne_data.T, labels)).T
    tsne_df = pd.DataFrame(data=tsne_data, columns=("Dim_1", "Dim_2", "label"))

    # Plotting the result of tsne
    sns.FacetGrid(tsne_df, hue="label", size=6).map(plt.scatter, 'Dim_1',
    'Dim_2').add_legend()
    plt.show()