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I am currently working on analyzing online reviews. I would like to try GuidedLDA (https://medium.freecodecamp.org/how-we-changed-unsupervised-lda-to-semi-supervised-guidedlda-e36a95f3a164) as some of the topics overlap. I have successfully installed the package. However, I am not sure on how to generate the document term matrix (referred to as X in the code in the website) and vocab using the excel document as inputs. Can someone please help with this? I tried to search online in various forums and did not find anything that was working.

June Shelter
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1 Answers1

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From textmining package ,excerpt of TDM Class

import re

import csv

import os

'''

import stemmer

'''

you can save the below code as a separate python file and import it as a regular module in your code e.g create_tdm.py

import create_tdm

X = create_tdm.TermDocumentMatrix("your Text ")

''' for Vocab '''

word2id = dict((v, idx) for idx, v in enumerate(" your text" ))

'''

Make sure list of guided words should be there in your text else you will get key error , just to check import pandas as pd

c = pd.DataFrame(list(word2id))

'''

class TermDocumentMatrix(object):

"""
Class to efficiently create a term-document matrix.

The only initialization parameter is a tokenizer function, which should
take in a single string representing a document and return a list of
strings representing the tokens in the document. If the tokenizer
parameter is omitted it defaults to using textmining.simple_tokenize

Use the add_doc method to add a document (document is a string). Use the
write_csv method to output the current term-document matrix to a csv
file. You can use the rows method to return the rows of the matrix if
you wish to access the individual elements without writing directly to a
file.

"""

def __init__(self, tokenizer=simple_tokenize):
    """Initialize with tokenizer to split documents into words."""
    # Set tokenizer to use for tokenizing new documents
    self.tokenize = tokenizer
    # The term document matrix is a sparse matrix represented as a
    # list of dictionaries. Each dictionary contains the word
    # counts for a document.
    self.sparse = []
    # Keep track of the number of documents containing the word.
    self.doc_count = {}

def add_doc(self, document):
    """Add document to the term-document matrix."""
    # Split document up into list of strings
    words = self.tokenize(document)
    # Count word frequencies in this document
    word_counts = {}
    for word in words:
        word_counts[word] = word_counts.get(word, 0) + 1
    # Add word counts as new row to sparse matrix
    self.sparse.append(word_counts)
    # Add to total document count for each word
    for word in word_counts:
        self.doc_count[word] = self.doc_count.get(word, 0) + 1

def rows(self, cutoff=2):
    """Helper function that returns rows of term-document matrix."""
    # Get master list of words that meet or exceed the cutoff frequency
    words = [word for word in self.doc_count \
      if self.doc_count[word] >= cutoff]
    # Return header
    yield words
    # Loop over rows
    for row in self.sparse:
        # Get word counts for all words in master list. If a word does
        # not appear in this document it gets a count of 0.
        data = [row.get(word, 0) for word in words]
        yield data

def write_csv(self, filename, cutoff=2):
    """
    Write term-document matrix to a CSV file.

    filename is the name of the output file (e.g. 'mymatrix.csv').
    cutoff is an integer that specifies only words which appear in
    'cutoff' or more documents should be written out as columns in
    the matrix.

    """
    f = csv.writer(open(filename, 'wb'))
    for row in self.rows(cutoff=cutoff):
        f.writerow(row)
Mahendra
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