Question

我正在基于用户评论（基于内容）开发旅游推荐系统。

我分析了数据集（515K CVS数据库）中的每个用户评论，所以我的问题是我需要分析每个酒店的评论，如何收集每个酒店的评论然后进行分析，反之亦然

这是我的代码，您能告诉我出什么问题了吗，您是否建议我安装特定的库？

reviews_df["review"] = reviews_df["Negative_Review"] + reviews_df["Positive_Review"]

# create the label 1=rating<5 , 0=rating>5
reviews_df["is_bad_review"] = reviews_df["Reviewer_Score"].apply(lambda x: 1 if x < 5 else 0)
# select only relevant columns
reviews_df = reviews_df[["review", "is_bad_review"]]
print (reviews_df.head())


# Reviews data is sampled in order to speed up computations.
reviews_df = reviews_df.sample(frac = 0.1, replace = False, random_state=42)

#Clean data
# remove 'No Negative' or 'No Positive' from text
reviews_df["review"] = reviews_df["review"].apply(lambda x: x.replace("No Negative", "").replace("No Positive", ""))

# return the wordnet object value corresponding to the POS tag
from nltk.corpus import wordnet

def get_wordnet_pos(pos_tag):
    if pos_tag.startswith('J'):
        return wordnet.ADJ
    elif pos_tag.startswith('V'):
        return wordnet.VERB
    elif pos_tag.startswith('N'):
        return wordnet.NOUN
    elif pos_tag.startswith('R'):
        return wordnet.ADV
    else:
        return wordnet.NOUN


import string
from nltk import pos_tag
from nltk.corpus import stopwords
from nltk.tokenize import WhitespaceTokenizer
from nltk.stem import WordNetLemmatizer

def clean_text(text):
    # lower text
    text = text.lower()
    # tokenize text and remove puncutation
    text = [word.strip(string.punctuation) for word in text.split(" ")]
    # remove words that contain numbers
    text = [word for word in text if not any(c.isdigit() for c in word)]
    # remove stop words
    stop = stopwords.words('english')
    text = [x for x in text if x not in stop]
    # remove empty tokens
    text = [t for t in text if len(t) > 0]
    # pos tag text
    pos_tags = pos_tag(text)
    # lemmatize text
    text = [WordNetLemmatizer().lemmatize(t[0], get_wordnet_pos(t[1])) for t in pos_tags]
    # remove words with only one letter
    text = [t for t in text if len(t) > 1]
    # join all
    text = " ".join(text)
    return(text)

# clean text data
reviews_df["review_clean"] = reviews_df["review"].apply(lambda x: clean_text(x))

# add sentiment anaylsis columns
from nltk.sentiment.vader import SentimentIntensityAnalyzer
sid = SentimentIntensityAnalyzer()
reviews_df["sentiments"] = reviews_df["review"].apply(lambda x: sid.polarity_scores(x))
reviews_df = pd.concat([reviews_df.drop(['sentiments'], axis=1), reviews_df['sentiments'].apply(pd.Series)], axis=1)

# add number of characters column
reviews_df["nb_chars"] = reviews_df["review"].apply(lambda x: len(x))

# add number of words column
reviews_df["nb_words"] = reviews_df["review"].apply(lambda x: len(x.split(" ")))

# add tf-idfs columns
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf = TfidfVectorizer(min_df = 10)
tfidf_result = tfidf.fit_transform(reviews_df["review_clean"]).toarray()
tfidf_df = pd.DataFrame(tfidf_result, columns = tfidf.get_feature_names())
tfidf_df.columns = ["word_" + str(x) for x in tfidf_df.columns]
tfidf_df.index = reviews_df.index
reviews_df = pd.concat([reviews_df, tfidf_df], axis=1)

用户评论的情绪分析

0 个答案: