Gensim: TypeError: doc2bow expects an array of unicode tokens on input, not a single string

Question

I am starting with some python task, I am facing a problem while using gensim. I am trying to load files from my disk and process them (split them and lowercase() them)

Answer 1

Dictionary needs a tokenized strings for its input:

dataset = ['driving car ',
           'drive car carefully',
           'student and university']

# be sure to split sentence before feed into Dictionary
dataset = [d.split() for d in dataset]

vocab = Dictionary(dataset)

Answer 2

Hello everyone i ran into the same problem. This is what worked for me

    #Tokenize the sentence into words
    tokens = [word for word in sentence.split()]

    #Create dictionary
    dictionary = corpora.Dictionary([tokens])
    print(dictionary)

Answer 3

In dictionary.py, the initialize function is:

def __init__(self, documents=None):
    self.token2id = {} # token -> tokenId
    self.id2token = {} # reverse mapping for token2id; only formed on request, to save memory
    self.dfs = {} # document frequencies: tokenId -> in how many documents this token appeared

    self.num_docs = 0 # number of documents processed
    self.num_pos = 0 # total number of corpus positions
    self.num_nnz = 0 # total number of non-zeroes in the BOW matrix

    if documents is not None:
        self.add_documents(documents)

Function add_documents Build dictionary from a collection of documents. Each document is a list of tokens:

def add_documents(self, documents):

    for docno, document in enumerate(documents):
        if docno % 10000 == 0:
            logger.info("adding document #%i to %s" % (docno, self))
        _ = self.doc2bow(document, allow_update=True) # ignore the result, here we only care about updating token ids
    logger.info("built %s from %i documents (total %i corpus positions)" %
                 (self, self.num_docs, self.num_pos))

So ,if you initialize Dictionary in this way, you must pass documents but not a single document. For example,

dic = corpora.Dictionary([a.split()])

is OK.