Would it be possible to look for texts that are within a certain topic (determined by LDA)?
I have a list of 5 topics with 10 words each, found by using lda.
I have analysed the texts in a dataframe's column. I would like to select/filter rows/texts that are in one specific topic.
If you need more information, I will provide you.
What I am referring to is the step that returns this output:
[(0,
'0.207*"house" + 0.137*"apartment" + 0.118*"sold" + 0.092*"beach" + '
'0.057*"kitchen" + 0.049*"rent" + 0.033*"landlord" + 0.026*"year" + '
'0.024*"bedroom" + 0.023*"home"'),
(1,
'0.270*"school" + 0.138*"homeworks" + 0.117*"students" + 0.084*"teacher" + '
'0.065*"pen" + 0.038*"books" + 0.022*"maths" + 0.020*"exercise" + '
'0.020*"friends" + 0.020*"college"'),
... ]
created by
# LDA Model
lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
id2word=id2word,
num_topics=num_topics,
random_state=100,
update_every=1,
chunksize=100,
passes=10,
alpha='auto',
# alpha=[0.01]*num_topics,
per_word_topics=True,
eta=[0.01]*len(id2word.keys()))
from pprint import pprint
pprint(lda_model.print_topics())
doc_lda = lda_model[corpus]
The original column with texts that have been analysed is called Texts
and it looks like:
Texts
"Children are happy to go to school..."
"The average price for buying a house is ... "
"Our children love parks so we should consider to buy an apartment nearby"
etc etc...
My expected output would be
Texts Topic
"Children are happy to go to school..." 2
"The average price for buying a house is ... " 1
"Our children love parks so we should consider to buy an apartment nearby"
2
Thanks
doc_lda
contains a list of (topic, score) tuple for each sentence. Hence you can flexibly assign a topic to the sentence using any heuristics, for example a simple heuristic would by assigning the topic which has the maximum score.
We can extract the topic scores of each sentence by doing this:
topic_scores = [[topic_score[1] for topic_score in sent] for sent in doc_lda]
You can also convert the above into a pandas dataframe where each row is a sentence and each column is the topic id. The dataframe data structure usually allows for a flexible and more complex operation on the topic-score sentence relationships
df_topics = pd.DataFrame(topic_scores)
If you just want to assign a single topic which has the maximum score on a sentence, you can do this:
max_topics = [max(sent, key=lambda x: x[1])[0] for sent in doc_lda]
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