我是一个初学者，我正在尝试对多句文本进行聚类，但我的结果很糟糕。有什么建议可以提高我的成绩吗？

import pandas
import pprint
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score
from sklearn.feature_extraction.text import TfidfVectorizer

dataset = pandas.read_csv('text.csv', encoding = 'utf-8')

comments = dataset['comments']

comments_list = comments.values.tolist()

vetorize = TfidfVectorizer()
X = vetorize.fit_transform(comments_list)

clusters_number = 6
model = KMeans(n_clusters = clusters_number, init = 'k-means++', max_iter = 300, n_init = 1)

model.fit(X)

centers = model.cluster_centers_
labels = model.labels_

clusters = {}
for verbatim, label in zip(verbatim_list, labels):
    try:
        clusters[str(label)].append(verbatim)
    except:
        clusters[str(label)] = [verbatim]
pprint.pprint(clusters)

#Top terms for cluster
print("Top termos par cluster:")
ordem_centroides = model.cluster_centers_.argsort()[:, ::-1]
termos = vetorizar.get_feature_names()
for i in range(clusters_number):
    print ("Cluster %d:" % i,)
    for ind in ordem_centroides[i, :10]:
        print (' %s' % termos[ind],)
        print()

我在不同的集群中有许多不同的主题。我预处理了我的数据（停用词，小写，我删除了点...）。但我仍然在一个集群中“喜欢取消订单”，在另一个集群中“喜欢取消订单”。实际上，理想的情况是将所有“取消订单”加入一个集群中。