Classification: how to handle reviews/long english words in feature set with all other numerical features
One of the ways to address your use case could be to create 2 separate models, one model using your text data features and another one using your numerical features and combine their results using ensembling.

The other way could be to create numeric features out of your text features (e.g. tf-idf, word2vec) and combine them with your numeric features and feed them to your model.

Sample code-

from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import pandas as pd

words = ['i hv paid','i dont like','its good','yum yum']
tf = TfidfVectorizer()
vector = tf.fit_transform(words)
df = pd.DataFrame([10,20,30,40])
df2 = pd.DataFrame(vector.toarray())
pd.concat([df,df2], axis=1)


Sample output-