Text Classification: What is It and How Does It Work?
Text classification is a powerful tool used in natural language processing (NLP) to automatically organize, filter and store text data. From sentiment analysis to email spam identification, text classification is integral to many applications of NLP. In this article, we’ll take a look at what text classification is, how it works and some of the ways it can be used to make life easier.
What is Text Classification?
Text classification is the process of assigning categories or labels to text according to its content. It is a form of supervised learning, meaning that a system is trained using a set of pre-labeled examples in order to classify new, unlabeled text.
Text classification is an important task in the field of natural language processing (NLP). It is used for a variety of tasks, such as sentiment analysis, email spam identification, organization of websites by search engines and topic classification of news articles.
How Does Text Classification Work?
Text classification is a two-step process. First, the text input is preprocessed and all of the text features are extracted. The extracted features are then used to train a deep learning model. Once trained, the model can be used to make predictions on new text inputs.
The most common natural language processing classifier consists of prediction and training. In the training process, the text is processed, and all of the text features are created. Once the deep learning model acquires the text features, it can use them to make predictions when compared to new text that is inputted.
Using Python’s textblob library, you can build a natural language processing classifier using a Naive Bayes classifier. TextBlob is a very easy-to-use natural language processing tool API commonly used in many NLP problems.
Python’s scikit-learn library also provides a pipeline natural language processing framework you can use for text classification. You must first prepare your data for the SVM model using the same text corpus and training corpus from the Naive Bayes example. After that is complete, you must create and train your feature vectors, and then apply a model to your test data and perform classification with SVM.
The text classification models are dependent upon the quantity and quality of the model features. It is always a good idea to include more training data when applying deep learning models. You can always improve your natural language processing classifiers using several different techniques such as text similarity, matching or coherence resolution.
Text Matching
Text matching is an important area of natural language processing. Text matching or text similarity applications include data de-duplication, automatic spelling correction and genome analysis. There are several text matching techniques available, including Levenshtein Distance, Phonetic Matching, Cosine Similarity and Flexible String Matching.
Levenshtein Distance
Levenshtein Distance is a method of representing the distance between two strings. This is defined as the minimum number of edits required for transforming one string into another. The number of allowable operations includes substitution, insertion or deletion of a single character.
You can implement Levenshtein Distance to perform efficient memory calculations as follows:
def levenshtein(s1, s2):
if len(s1) > len(s2):
s1, s2 = s2, s1
distances = range(len(s1) + 1)
for index2, char2 in enumerate(s2):
newDistances = [index2 + 1]
for index1, char1 in enumerate(s1):
if char1 == char2:
newDistances.append(distances[index1])
else:
newDistances.append(1 + min((distances[index1],
distances[index1 + 1], newDistances[-1])))
distances = newDistances
return distances[-1]
print(levenshtein(“analyze”, “analyse”))
Phonetic Matching
Phonetic Matching is another common text matching technique. A typical phonetic matching model takes keywords such as location name or person’s name as input and produces character strings that identify collections of words that are phonetically similar. Phonetic matching is a useful technique when it comes to searching through large text corpora, matching relevant names or correcting spelling errors.
Two main phonetic matching algorithms are Metaphone and Soundex. You can also use the popular Python Module Fuzzy to compute certain Soundex strings for words as follows:
import fuzzy
soundex = fuzzy.Soundex(4)
print soundex(‘ankit’)
print soundex(‘aunkit’)
Flexible String Matching
Flexible string matching is another common text matching technique. A complete text matching system usually consists of different models that are pipelined together to compute a variety of different text corpora. Regular expressions are very useful in this case. Other common text matching techniques include lemmatized matching, extract matching and compact matching, which process punctuation, slangs and spaces.
When you have text represented as a vector notation, you must use a text matching technique called Cosine Similarity to measure the vectorized similarities. You can easily convert your text to vectors using term frequency and apply Cosine Similarity between the two text pieces you obtained as follows:
import math
from collections import Counter
def get_cosine(vec1, vec2):
common = set(vec1.keys()) & set(vec2.keys())
numerator = sum([vec1[x] * vec2[x] for x in common])
sum1 = sum([vec1[x]2 for x in vec1.keys()]) sum2 = sum([vec2[x]2 for x in vec2.keys()])
denominator = math.sqrt(sum1) * math.sqrt(sum2)
if not denominator:
return 0.0
else:
return float(numerator) / denominator
def text_to_vector(text):
words = text.split()
return Counter(words)
text1 = ‘Learning Natural Language Processing’
text2 = ‘Natural Language Processing in Python Guide’
vector1 = text_to_vector(text1)
vector2 = text_to_vector(text2)
cosine = get_cosine(vector1, vector2)
print cosine
Coreference Resolution
Coreference resolution is another common text matching method. Coreference resolution is the process of finding relational links between the words within sentences in textual data. It is used for a variety of tasks, such as question answering, document summarization and information extraction. For commercial purposes, there is a Python wrapper provided by Stanford CoreNLP in which you can practice coreference resolution on your text data.
Conclusion
Text classification is an important tool in natural language processing. It is used to automatically organize, filter and store text data. From sentiment analysis to email spam identification, text classification is integral to many applications of NLP. Text classification is a two-step process, consisting of feature extraction and model training. Python’s textblob and scikit-learn libraries are popular tools used for text classification. Text matching is also an important area of natural language processing and is used for a variety of tasks, such as data de-duplication, automatic spelling correction and genome analysis. Levenshtein Distance, Phonetic Matching, Cosine Similarity and Flexible String Matching are some of the text matching techniques used in NLP. Coreference resolution is also used in NLP to find relational links between words in sentences.
Text classification, text matching and coreference resolution are important tools used in natural language processing. With the proper tools and techniques, you can easily build powerful text classification models and use them for a variety of tasks.