Topic modeling
Topic modeling is a technique in Natural Language Processing that involves discovering the underlying topics or themes in a large corpus of text data, such as news articles or social media posts. The goal of topic modeling is to automatically identify patterns in the text data and group similar documents together based on their topics.
One popular algorithm for topic modeling is Latent Dirichlet Allocation (LDA). Here is an example of how to perform topic modeling using LDA in Python:
import pandas as pd
import gensim
from gensim.utils import simple_preprocess
from gensim.models.ldamodel import LdaModel
from gensim.corpora.dictionary import Dictionary
# Load the data
data = pd.read_csv('news_articles.csv')
# Tokenize the text
tokens = data['text'].apply(simple_preprocess)
# Create a dictionary from the tokens
dictionary = Dictionary(tokens)
# Convert the tokens into a bag-of-words representation
corpus = [dictionary.doc2bow(t) for t in tokens]
# Train the LDA model
lda_model = LdaModel(corpus=corpus, id2word=dictionary, num_topics=10)
# Print the topics and their top words
for topic in lda_model.print_topics():
print(topic)
In this example, we start by loading the data from a CSV file that contains news articles. Next, we tokenize the text using the simple_preprocess function from the gensim library, which converts each document into a list of tokens. We then create a dictionary from the tokens and convert the tokens into a bag-of-words representation using the doc2bow function.
We then train the LDA model using the LdaModel function from gensim, which takes the corpus, dictionary, and the number of topics as input. Finally, we print the top words for each topic using the print_topics function.
The output of the code will be the top words for each of the 10 topics discovered by the LDA model. For example:
0. 0.030*"government" + 0.020*"minister" + 0.018*"party" + 0.017*"election" + 0.015*"political" + 0.013*"leader" + 0.012*"vote" + 0.011*"campaign" + 0.010*"power" + 0.009*"country"
1. 0.025*"company" + 0.020*"business" + 0.015*"market" + 0.014*"product" + 0.012*"industry" + 0.011*"customer" + 0.010*"service" + 0.010*"sale" + 0.009*"new" + 0.008*"technology"
2. 0.023*"city" + 0.020*"building" + 0.017*"area" + 0.015*"project" + 0.013*"development" + 0.012*"street" + 0.011*"property" + 0.011*"community" + 0.010*"public" + 0.009*"plan"
3. 0.029*"school" + 0.024*"student" + 0.018*"education" + 0.017*"teacher" + 0.013*"program" + 0.012*"class" + 0.011*"university" + 0.009*"college" + 0.008*"learning" + 0.008*"graduate"
4. 0.018*"health" + 0.014*"care" + 0.012
5. 0.025*"team" + 0.022*"game" + 0.018*"player" + 0.015*"season" + 0.014*"win" + 0.012*"sport" + 0.011*"coach" + 0.010*"play" + 0.009*"score" + 0.009*"league"
6. 0.023*"film" + 0.016*"movie" + 0.012*"director" + 0.011*"story" + 0.010*"character" + 0.009*"book" + 0.009*"screenplay" + 0.008*"actor" + 0.007*"award" + 0.007*"cinema"
7. 0.019*"music" + 0.016*"album" + 0.012*"song" + 0.011*"band" + 0.009*"performance" + 0.008*"artist" + 0.008*"rock" + 0.008*"record" + 0.007*"guitar" + 0.006*"singer"
8. 0.028*"police" + 0.017*"man" + 0.014*"woman" + 0.013*"arrest" + 0.013*"officer" + 0.012*"charge" + 0.011*"crime" + 0.011*"court" + 0.010*"investigation" + 0.009*"victim"
9. 0.020*"food" + 0.018*"restaurant" + 0.013*"menu" + 0.012*"dish" + 0.010*"flavor" + 0.009*"chef" + 0.009*"ingredient" + 0.008*"cook" + 0.008*"meal" + 0.008*"wine"
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