1. Introduction to Text
Classification
Text classification is the process of categorizing and assigning labels to a
piece of text based on its content. It is a fundamental part of natural
language processing (NLP) and machine learning, with wide-ranging
applications from sentiment analysis to spam filtering.
LT by Logeswari T
2. Types of Text Classification
Binary Classification
In binary classification, the text is classified into
exactly two categories, such as spam vs. not spam
or positive sentiment vs. negative sentiment.
Multi-class Classification
This involves categorizing text into three or more
predefined classes, such as categorizing news
articles into politics, sports, and entertainment.
3. Supervised Learning for Text
Classification
1 Training Data
Supervised learning for text
classification requires labeled
training data, where the input text is
paired with the correct category or
label.
2 Algorithms
Popular supervised learning
algorithms for text classification
include Naive Bayes, Support Vector
Machines (SVM), and Logistic
Regression.
4. Unsupervised Learning for Text
Classification
1 Clustering
Unsupervised learning techniques use
clustering algorithms to group similar texts
together based on their content without pre-
existing labels.
2 Topic Modeling
Algorithms like Latent Dirichlet Allocation
(LDA) are used for uncovering the hidden
topics, which can assist in text categorization.
5. Feature Extraction for Text Classification
1
Bag of Words Model
2
TF-IDF
3
Word Embeddings
6. Evaluation Metrics for Text Classification
1 Precision, Recall, and F1 Score
These metrics are commonly used to
evaluate the performance of text classification
models, considering both correctness and
completeness of the predictions.
2 Confusion Matrix
It provides a detailed breakdown of correct
and incorrect classifications, helping in
understanding model behavior.
7. Applications of Text Classification
1 Sentiment Analysis
Automatically determining sentiment polarity, for example, whether product
reviews are positive, negative, or neutral.
2 Spam Filtering
Identifying and filtering out unsolicited and unwanted messages.
3 Document Classification
Organizing and categorizing documents based on their content, such as
legal documents, news articles, or academic papers.
8. Future of Text Classification
Advanced NLP Models
The development of more
powerful and efficient NLP
models is likely to enhance the
accuracy and capabilities of
text classification systems.
Explainable AI
Efforts will be made to develop
text classification models that
are more transparent and
provide explanations for the
predictions they make.
Industry-Specific
Solutions
The customization of text
classification models for
industry-specific tasks and
domains is expected to
become more prevalent.
