doc2bow() only includes terms that actually occur: terms that do not occur in a document will not appear in that document’s vector. The tuples are (term ID, term frequency) pairs, so if print(dictionary.token2id) says brocolli’s id is 0, then the first tuple indicates that brocolli appeared twice in doc_a. This list of tuples represents our first document, doc_a. As an example, print(corpus) results in the following: > print(corpus)
#CLEAN TEXT WITH GENSIM SERIES#
In each document vector is a series of tuples. The result, corpus, is a list of vectors equal to the number of documents. The doc2bow() function converts dictionary into a bag-of-words.
Next, our dictionary must be converted into a bag-of-words: corpus = To see each token’s unique integer id, try print(dictionary.token2id). The Dictionary() function traverses texts, assigning a unique integer id to each unique token while also collecting word counts and relevant statistics. To do that, we need to construct a document-term matrix with a package called gensim: from gensim import corpora, models To generate an LDA model, we need to understand how frequently each term occurs within each document. So now texts is a list of lists, one list for each of our original documents. Let’s fast forward and imagine that we looped through all our documents and appended each one to texts. The result of our cleaning stage is texts, a tokenized, stopped and stemmed list of words from a single document. In our example, not much happened: likes became like. p_stemmer returns the string parameter in stemmed form, so we need to loop through our stopped_tokens: # stem token Note that p_stemmer requires all tokens to be type str. # Create p_stemmer of class PorterStemmer To implement a Porter stemming algorithm, import the Porter Stemmer module from NLTK: from import PorterStemmer
The Porter stemming algorithm is the most widely used method. Like stopping, stemming is flexible and some methods are more aggressive. For example, “stemming,” “stemmer,” “stemmed,” all have similar meanings stemming reduces those terms to “stem.” This is important for topic modeling, which would otherwise view those terms as separate entities and reduce their importance in the model. Stemming words is another common NLP technique to reduce topically similar words to their root. Removing stop words is now a matter of looping through our tokens and comparing each word to the en_stop list. We can call get_stop_words() to create a list of stop words: from stop_words import get_stop_words In our case, we are using the stop_words package from Pypi, a relatively conservative list. You can always construct your own stop word list or seek out another package to fit your use case. For example, if we’re topic modeling a collection of music reviews, then terms like “The Who” will have trouble being surfaced because “the” is a common stop word and is usually removed. The definition of a stop word is flexible and the kind of documents may alter that definition. These terms are called stop words and need to be removed from our token list. Our document from doc_a is now a list of tokens.Ĭertain parts of English speech, like conjunctions (“for”, “or”) or the word “the” are meaningless to a topic model. Check the script at the end of this page for an example. You’ll need to create a for loop to traverse all your documents. Note: this example calls tokenize() on a single document. For unique use cases, it’s better to use regex and iterate until your document is accurately tokenized. This is a simple solution, but can cause problems for words like “don’t” which will be read as two tokens, “don” and “t.” NLTK provides a number of pre-constructed tokenizers like.
#CLEAN TEXT WITH GENSIM CODE#
The above code will match any word characters until it reaches a non-word character, like a space. Tokenization can be performed many ways–we are using NLTK’s tokenize.regexp module: from nltk.tokenize import RegexpTokenizer
In this case, we are interested in tokenizing to words. Tokenization segments a document into its atomic elements.
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