def count_words(df,label): word_dic={} for index,item in df[df['sentiment_value']==label].iterrows(): for i in item.after_cut_content: if i not in word_dic: word_dic[i]=1 else: word_dic[i]+=1 return word_dic
时间: 2024-02-14 15:03:53 浏览: 28
这段代码是一个函数,名为 count_words,它接受两个参数:一个 pandas 数据框 df 和一个 label。函数的功能是统计数据框 df 中 sentiment_value 等于 label 的行的 after_cut_content 属性中每个单词出现的次数,并将结果存储在一个字典 word_dic 中,并返回该字典。
具体实现上,函数首先创建一个空字典 word_dic,然后对于 df 中 sentiment_value 等于 label 的每一行,获取其 after_cut_content 属性,并对其进行遍历。对于遍历到的每个单词,判断其是否已经在 word_dic 字典中,如果不在,则将其加入字典,并将对应的 value 置为 1;如果已经在字典中,则将其对应的 value 加 1。最后返回生成的 word_dic 字典,该字典记录了数据框中所有 sentiment_value 等于 label 的行的 after_cut_content 属性中出现的单词及其出现的次数。
相关问题
def calculate_sentiment(words): sentiment = 0 count = 0 negation = False for i, word in enumerate(words): if word in negative_words: negation = not negation if word in degree_words: degree = degree_words[word] if i > 0 and words[i-1] in ['不', '没', '非常', '十分', '极其', '太', '特别', '超级', '尤其', '相当', '异常', '略微']: degree = -degree else: degree = 1 if word in sentiment_dict: if negation: sentiment -= sentiment_dict[word] * degree else: sentiment += sentiment_dict[word] * degree count += degree if count == 0: return 0 else: return sentiment / count请告诉我这段代码如何进行计算情感得分的
这段代码是一个简单的情感分析算法,用于计算一段文字的情感得分。算法首先会遍历文字中的每个词语,如果词语是消极情感词,则将negation标记为True;如果词语是程度副词,则根据前一个词语是否为否定词来确定程度的正负;如果词语是情感词,则根据negation标记和程度副词的程度值来计算情感得分。计算完所有词语的情感得分后,算法将情感得分的总和除以程度副词的数量得到最终的情感得分。
X_train = df.loc[:25000, 'review'].values y_train = df.loc[:25000, 'sentiment'].values X_test = df.loc[25000:, 'review'].values y_test = df.loc[25000:, 'sentiment'].values from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import GridSearchCV tfidf = TfidfVectorizer(strip_accents=None, lowercase=False, preprocessor=None) param_grid = [{'vect__ngram_range': [(1, 1)], 'vect__stop_words': [stop, None], 'vect__tokenizer': [tokenizer, tokenizer_porter], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0]}, {'vect__ngram_range': [(1, 1)], 'vect__stop_words': [stop, None], 'vect__tokenizer': [tokenizer, tokenizer_porter], 'vect__use_idf':[False], 'vect__norm':[None], 'clf__penalty': ['l1', 'l2'], 'clf__C': [1.0, 10.0, 100.0]}, ] lr_tfidf = Pipeline([('vect', tfidf), ('clf', ******)]) # find out how to use pipeline and choose a model to make the document classification gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid, scoring='accuracy', cv=5, verbose=2, n_jobs=-1) *号部分填什么
You can choose a classifier to use in the pipeline depending on your specific task and the nature of your data. Some commonly used classifiers for document classification include logistic regression, support vector machines (SVM), and naive Bayes.
For example, if you want to use logistic regression as your classifier, you can replace the asterisks with `LogisticRegression(random_state=0)`. The `random_state` parameter ensures that the results are reproducible.
The complete code would look like this:
```
from sklearn.linear_model import LogisticRegression
X_train = df.loc[:25000, 'review'].values
y_train = df.loc[:25000, 'sentiment'].values
X_test = df.loc[25000:, 'review'].values
y_test = df.loc[25000:, 'sentiment'].values
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import GridSearchCV
tfidf = TfidfVectorizer(strip_accents=None,
lowercase=False,
preprocessor=None)
param_grid = [{'vect__ngram_range': [(1, 1)],
'vect__stop_words': [stop, None],
'vect__tokenizer': [tokenizer, tokenizer_porter],
'clf__penalty': ['l1', 'l2'],
'clf__C': [1.0, 10.0, 100.0]},
{'vect__ngram_range': [(1, 1)],
'vect__stop_words': [stop, None],
'vect__tokenizer': [tokenizer, tokenizer_porter],
'vect__use_idf':[False],
'vect__norm':[None],
'clf__penalty': ['l1', 'l2'],
'clf__C': [1.0, 10.0, 100.0]},
]
lr_tfidf = Pipeline([('vect', tfidf),
('clf', LogisticRegression(random_state=0))])
gs_lr_tfidf = GridSearchCV(lr_tfidf, param_grid,
scoring='accuracy',
cv=5,
verbose=2,
n_jobs=-1)
```