TopicModel: How to query documents by topic model

问题:

Below I created a full reproducible example to compute the topic model for a given DataFrame.

import numpy as np  
import pandas as pd

data = pd.DataFrame({'Body': ['Here goes one example sentence that is generic',
                  'My car drives really fast and I have no brakes',
                  'Your car is slow and needs no brakes', 
                  'Your and my vehicle are both not as fast as the airplane']})

from sklearn.decomposition import LatentDirichletAllocation
from sklearn.feature_extraction.text import CountVectorizer

vectorizer = CountVectorizer(lowercase = True, analyzer = 'word')

data_vectorized = vectorizer.fit_transform(data.Body)
lda_model = LatentDirichletAllocation(n_components=4, 
                                      learning_method='online', 
                                      random_state=0,
                                      verbose=1)
lda_topic_matrix = lda_model.fit_transform(data_vectorized)

Question: How is it possible to filter documents by topic? If so, can documents have multiple topic tags, or is a threshold needed?

In the end, I like to tag every document with "1" depending on whether it has a high loading of topic 2 and topic 3, else "0".

回答1:

lda_topic_matrix contains distribution of probabilities of a document to belong to specific topic/tag. In human it means that each row sums to 1, while the value at each index is a probability of that document to belong to a specific topic. So, each document does have all topics tags, with different degree. In case you have 4 topics, the document that has all tags equally will have a corresponding row in lda_topic_matrix similar to [0.25, 0.25, 0.25, 0.25]. And the row of a document with only single topic ("0") will become something like [0.97, 0.01, 0.01, 0.01] and document with two topics ("1" and "2") will have a distribution like [0.01, 0.54, 0.44, 0.01]

So the most simplistic approach is to select the topic with the highest probability and check whether it is 2 or 3:

main_topic_of_document = np.argmax(lda_topic_matrix, axis=1)
tagged = ((main_topic_of_document==2) | (main_topic_of_document==3)).astype(np.int64)

This article provides a good explanation on inner mechanics of LDA.