使用python，讲评论文本数据中相似的文本进行删除，留下不相似的语句
import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
def remove_similar_comments(comments, threshold):
# 使用TF-IDF向量化文本数据
vectorizer = TfidfVectorizer()
vectors = vectorizer.fit_transform(comments)
# 计算余弦相似度矩阵
similarity_matrix = cosine_similarity(vectors)
# 根据相似度阈值移除相似的评论
removed_comments = []
for i in range(len(comments)):
is_similar = False
for j in range(i+1, len(comments)):
if similarity_matrix[i][j] >= threshold:
is_similar = True
break
if not is_similar:
removed_comments.append(comments[i])
return removed_comments
# 示例用法
comments = [
"这个产品真棒！",
"我很喜欢这个产品！",
"这个产品一般般。",
"我不太喜欢这个产品。",
"这个产品质量很好！"
]
threshold = 0.8
filtered_comments = remove_similar_comments(comments, threshold)
print("保留的评论：")
for comment in filtered_comments:
print(comment)
【以下回答由 GPT 生成】
要解决这个问题，我们需要比较文本之间的相似性，并删除相似的句子。通常，计算文本相似性涉及以下步骤： 1. 清洗文本数据，例如去除特殊符号、停用词等。 2. 对清洗后的文本进行分词处理。 3. 计算文本之间的相似性。 4. 根据相似性进行筛选，删除相似的句子。
根据问题的描述，我们可以采用以下步骤来实现程序：
nltk、gensim等。pip install nltk gensim
nltk的停用词和分词器。import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
nltk.download('stopwords')
nltk.download('punkt')
def clean_text(text):
# 去除特殊符号
text = text.lower().strip()
text = re.sub(r'[^\w\s]', '', text)
# 去除停用词
stop_words = set(stopwords.words('english'))
words = word_tokenize(text)
words = [word for word in words if word not in stop_words]
return ' '.join(words)
from gensim import corpora
from gensim.models import TfidfModel
from gensim.similarities import MatrixSimilarity
def calculate_similarity(texts):
# 构建词袋模型
corpus = [word_tokenize(text) for text in texts]
dictionary = corpora.Dictionary(corpus)
corpus = [dictionary.doc2bow(text) for text in corpus]
# 计算TF-IDF
tfidf = TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
# 构建索引
index = MatrixSimilarity(corpus_tfidf)
# 计算相似性
similarity_matrix = index[corpus_tfidf]
return similarity_matrix
def remove_similar_sentences(texts, similarity_matrix, threshold):
# 根据相似性阈值删除相似的句子
cleaned_texts = []
for i, row in enumerate(similarity_matrix):
similar_indices = [j for j, similarity in enumerate(row) if similarity > threshold]
cleaned_text = [text for j, text in enumerate(texts) if j not in similar_indices]
cleaned_texts.append(cleaned_text)
return cleaned_texts
import re
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from gensim import corpora
from gensim.models import TfidfModel
from gensim.similarities import MatrixSimilarity
def clean_text(text):
# 去除特殊符号
text = text.lower().strip()
text = re.sub(r'[^\w\s]', '', text)
# 去除停用词
stop_words = set(stopwords.words('english'))
words = word_tokenize(text)
words = [word for word in words if word not in stop_words]
return ' '.join(words)
def calculate_similarity(texts):
# 构建词袋模型
corpus = [word_tokenize(text) for text in texts]
dictionary = corpora.Dictionary(corpus)
corpus = [dictionary.doc2bow(text) for text in corpus]
# 计算TF-IDF
tfidf = TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
# 构建索引
index = MatrixSimilarity(corpus_tfidf)
# 计算相似性
similarity_matrix = index[corpus_tfidf]
return similarity_matrix
def remove_similar_sentences(texts, similarity_matrix, threshold):
# 根据相似性阈值删除相似的句子
cleaned_texts = []
for i, row in enumerate(similarity_matrix):
similar_indices = [j for j, similarity in enumerate(row) if similarity > threshold]
cleaned_text = [text for j, text in enumerate(texts) if j not in similar_indices]
cleaned_texts.append(cleaned_text)
return cleaned_texts
# 示例用法
texts = [
"This is a sample sentence.",
"This is another sample sentence.",
"This is a similar sentence.",
"This sentence is not similar."
]
cleaned_texts = [clean_text(text) for text in texts]
similarity_matrix = calculate_similarity(cleaned_texts)
cleaned_texts = remove_similar_sentences(texts, similarity_matrix, 0.8)
print(cleaned_texts)
nltk的停用词和分词器。以上是使用Python删除文本中相似内容的解决方案。希望能对您有所帮助。如果您有任何问题，请随时提问。
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