![]() ![]() We have just used Gensim’s inbuilt version of the LDA algorithm, but there is an LDA model that provides better quality of topics called the LDA Mallet Model. Print( 'nCoherence Score: ', coherence_lda) # Compute Coherence ScoreĬoherence_model_lda = CoherenceModel(model=lda_model, texts=tweets, dictionary=id2word, coherence= 'c_v')Ĭoherence_lda = coherence_model_lda.get_coherence() A good model will generate topics with high topic coherence scores. Topic coherence evaluates a single topic by measuring the degree of semantic similarity between high scoring words in the topic. Our model will be better if the words in a topic are similar, so we will use topic coherence to evaluate our model. Let’s use pyLDAvis to visualize the topics: The first topic may be politics, and the second topic may be sport, but the pattern is not clear. What do these tuples mean? Let’s convert them into human readable format to understand:, freq) for i, freq in doc] for doc in corpus] is a great tool for this: id2word = Dictionary(tweets)Ĭorpus = We start with converting a collection of words to a bag of words, which is a list of tuples (word_id, word_frequency). If the model knows the word frequency, and which words often appear in the same document, it will discover patterns that can group different words together. Topic modeling involves counting words and grouping similar word patterns to describe topics within the data. # Turn the list of string into a list of tokens If you want to get access to the data above and follow along with the article, download the data and put the data in your current directory, then run: tweets = pd.read_csv( 'dp-export-8940.csv') #Change this with the name of your downloaded file Moving on, let’s import relevant libraries: import gensimįrom import CoherenceModelįrom import LdaModel The script to process the data can be found in Neptune app. Install pyLDAvis with: pip install pyldavis How to start with pyLDAvis and how to use it We’ll analyze a real Twitter dataset containing 6000 tweets. Pretty cool, isn’t it? Now we will learn how to use topic modeling and pyLDAvis to categorize tweets and visualize the results. ![]()
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