如何提升jieba分词效果以更好地提取景区评论中的关键词？

# 加载中文停用词
stop_words = set(stopwords.words('chinese'))
broadcastVar = spark.sparkContext.broadcast(stop_words)

# 中文文本分词
def tokenize(text):
    return list(jieba.cut(text))

# 删除中文停用词
def delete_stopwords(tokens, stop_words):
    filtered_words = [word for word in tokens if word not in stop_words]
    filtered_text = ' '.join(filtered_words)
    return filtered_text

# 删除标点符号和特定字符
def remove_punctuation(input_string):
    punctuation = string.punctuation + "！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.\t \n很好是去还不人太都中"
    translator = str.maketrans('', '', punctuation)
    no_punct = input_string.translate(translator)
    return no_punct

def Thematic_focus(text):
    from gensim import corpora, models
    num_words = min(len(text) // 50 + 3, 10) # 动态调整主题词数量

    tokens = tokenize(text)
    stop_words = broadcastVar.value
    text = delete_stopwords(tokens, stop_words)
    text = remove_punctuation(text)
    tokens = tokenize(text)

    dictionary = corpora.Dictionary([tokens])
    corpus = [dictionary.doc2bow(tokens)]
    lda_model = models.LdaModel(corpus, num_topics=1, id2word=dictionary, passes=50)
    topics = lda_model.show_topics(num_words=num_words)
    for topic in topics:
        return str(topic)