使用sklearn-python进行具有分类特征的多元线性回归

Question

I have a dataset, where each document possesses a corresponding score/ rating

dataset = [
   {"text":"I don't like this small device", "rating":"2"},
   {"text":"Really love this large device", "rating":"5"},
   ....
]

In addition, I have a category(variable) of term lists extracted out of text variables from the same dataset

x1 = [short, slim, small, shrink]
x2 = [big,huge,large]

So, how can I do the linear regression with multiple independent variables as a word lists( or varible representing the existence of any word from corresponding term list, because each term in lists is unique ) above and the dependent variable as a rating . In other words

how could I evaluate term lists impact on the rating with sklearn

I used TfidfVectorizer to derive the document-term matrix. If it's possible please provide simple code snippet or example.

Answer 1

Given the discussion in the comments, it seems that the interpretation should be that each list defines a binary variable whose value depends on whether or not any words from the list appear in the text in question. So, let us first change the texts so that the words actually appear:

dataset = [
   {"text": "I don't like this large device", "rating": "2"},
   {"text": "Really love this small device", "rating": "5"},
   {"text": "Some other text", "rating": "3"}
]

To simplify our work, we'll then load this data into a data frame, change the ratings to be integers, and create the relevant variables:

df = pd.DataFrame(dataset)
df['rating'] = df['rating'].astype(int)
df['text'] = df['text'].str.split().apply(set)
x1 = ['short', 'slim', 'small', 'shrink']
x2 = ['big', 'huge', 'large']
df['x1'] =  df.text.apply(lambda x: x.intersection(x1)).astype(bool)
df['x2'] =  df.text.apply(lambda x: x.intersection(x2)).astype(bool)

That is, at this point df is the following data frame:

   rating                                   text     x1     x2
0       2  {this, large, don't, like, device, I}  False   True
1       5    {this, small, love, Really, device}   True  False
2       3                    {other, Some, text}  False  False

With this, we can create the relevant model, and check what the coefficients end up being:

model = LinearRegression()
model.fit(df[['x1', 'x2']], df.rating)
print(model.coef_)  # array([ 2., -1.])
print(model.intercept_)  # 3.0

As also mentioned in the comments, this thing will produce at most four ratings, one for each of the combinations of x1 and x2 being True or False . In this case, it just so happens that all possible outputs are integers, but in general, they need not be, nor need they be confined to the interval of interest. Given the ordinal nature of the ratings, this is really a case for some sort of ordinal regression (cf. eg mord ).