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Text Classification Algorithms: A Survey
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Text Classification Algorithms: A Survey
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|UniversityCube| |DOI| |Best| |medium| |mendeley| |contributions-welcome| |arXiv| |ansicolortags| |contributors| |twitter|
.. figure:: docs/pic/WordArt.png
Referenced paper : `Text Classification Algorithms: A Survey <https://arxiv.org/abs/1904.08067>`__
|BPW|
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Table of Contents
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.. contents::
:local:
:depth: 4
============
Introduction
============
.. figure:: docs/pic/OverviewTextClassification.png
====================================
Text and Document Feature Extraction
====================================
----
Text feature extraction and pre-processing for classification algorithms are very significant. In this section, we start to talk about text cleaning since most of documents contain a lot of noise. In this part, we discuss two primary methods of text feature extractions- word embedding and weighted word.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Text Cleaning and Pre-processing
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In Natural Language Processing (NLP), most of the text and documents contain many words that are redundant for text classification, such as stopwords, miss-spellings, slangs, and etc. In this section, we briefly explain some techniques and methods for text cleaning and pre-processing text documents. In many algorithms like statistical and probabilistic learning methods, noise and unnecessary features can negatively affect the overall perfomance. So, elimination of these features are extremely important.
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Tokenization
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Tokenization is the process of breaking down a stream of text into words, phrases, symbols, or any other meaningful elements called tokens. The main goal of this step is to extract individual words in a sentence. Along with text classifcation, in text mining, it is necessay to incorporate a parser in the pipeline which performs the tokenization of the documents; for example:
sentence:
.. code::
After sleeping for four hours, he decided to sleep for another four
In this case, the tokens are as follows:
.. code::
{'After', 'sleeping', 'for', 'four', 'hours', 'he', 'decided', 'to', 'sleep', 'for', 'another', 'four'}
Here is python code for Tokenization:
.. code:: python
from nltk.tokenize import word_tokenize
text = "After sleeping for four hours, he decided to sleep for another four"
tokens = word_tokenize(text)
print(tokens)
-----------
Stop words
-----------
Text and document classification over social media, such as Twitter, Facebook, and so on is usually affected by the noisy nature (abbreviations, irregular forms) of the text corpuses.
Here is an exmple from `geeksforgeeks <https://www.geeksforgeeks.org/removing-stop-words-nltk-python/>`__
.. code:: python
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
example_sent = "This is a sample sentence, showing off the stop words filtration."
stop_words = set(stopwords.words('english'))
word_tokens = word_tokenize(example_sent)
filtered_sentence = [w for w in word_tokens if not w in stop_words]
filtered_sentence = []
for w in word_tokens:
if w not in stop_words:
filtered_sentence.append(w)
print(word_tokens)
print(filtered_sentence)
Output:
.. code:: python
['This', 'is', 'a', 'sample', 'sentence', ',', 'showing',
'off', 'the', 'stop', 'words', 'filtration', '.']
['This', 'sample', 'sentence', ',', 'showing', 'stop',
'words', 'filtration', '.']
---------------
Capitalization
---------------
Sentences can contain a mixture of uppercase and lower case letters. Multiple sentences make up a text document. To reduce the problem space, the most common approach is to reduce everything to lower case. This brings all words in a document in same space, but it often changes the meaning of some words, such aExcerpt of 179,182 characters
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matched fp:25ddd57fb20e51d8, topic:text-classification, readme:natural language processing, readme:tokenization
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