Machine Learning Text Analyzer – Text Classification Using Supervised And Un-supervised Algorithms

Machine Learning Text Analyzer – Text Classification Using Supervised And Un-supervised Algorithms. 

ABSTRACT  

Text analysis is a branch of data mining that deals with text documents. This project brings to light the classification of texts into their various categories. The structured and unstructured data seems to on a high rise in this era. Thus, to be able to classify this data is important.

Classification however starts from collection, preprocessing, and feature extraction. There are several techniques that can be used for text classification, but machine learning algorithms will be employed in this project. Because of the advent of Natural Language Processing, we will be able to see the need for feature extraction and selection.

In this research, we will be able to see how the computer intelligently classifies text into their various categories. Emphasis will be on English language word document. 

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TABLE OF CONTENT

ACKNOWLEDGEMENT……………………………………………………………………………………………… vii
Chapter1…………………………………………………………………………………………………………………… 3
1.1 Introduction………………………………………………………………………………………………….. 3
1.2 Natural Language Processing (NLP) ……………………………………………………………….. 4
1.3 Objectives of the Project………………………………………………………………………………… 5
1.4 Problem Statement……………………………………………………………………………………….. 6
1.5 Limitations of the Study …………………………………………………………………………………. 6
1.6 Chapter Organization…………………………………………………………………………………….. 7

Chapter 2………………………………………………………………………………………………………………….. 8
2.1 Introduction to Machine Learning…………………………………………………………………….. 8
2.1.1 Supervised Learning………………………………………………………………………………….. 9
2.1.2 Unsupervised Machine learning…………………………………………………………………..10
2.1.3 Applications of Machine Learning ………………………………………………………………..12
2.2 Literature Review………………………………………………………………………………………….13

Chapter 3………………………………………………………………………………………………………………….16
3.1 Classification Steps ………………………………………………………………………………………16
3.2 Project Requirements ……………………………………………………………………………………16
3.3 Data collection and preparation ………………………………………………………………………17
3.4 Preprocessing Data ………………………………………………………………………………………18
3.5 Feature Extraction ………………………………………………………………………………………..20
3.5.1 Vectorization…………………………………………………………………………………………….20
3.5.2 Classification Technique…………………………………………………………………………….22

Chapter 4………………………………………………………………………………………………………………….29
4.1 Text Representation ……………………………………………………………………………………..30
4.2 Test Classification and Preprocessing ……………………………………………………………..31
4.2.1 Precision………………………………………………………………………………………………….32
4.2.2 Recall ……………………………………………………………………………………………………..32
4.2.3 F1 score ………………………………………………………………………………………………….32
4.2.4 Accuracy………………………………………………………………………………………………….32
4.3 Results ……………………………………………………………………………………………………….33
4.3.1 Multinomial Naïve Bayes (MNB) ………………………………………………………………….33
4.3.2 Logistic Regression (LR)…………………………………………………………………………….34
4.3.3 Support Vector Machine (SVM) …………………………………………………………………..35
4.3.4 K-Nearest Neighbor (KNN) …………………………………………………………………………36
4.4 Comparison Result ……………………………………………………………………………………….37

Chapter 5……………………………………………………………………………………………………………39
5.1 Conclusion…………………………………………………………………………………………………..39
5.2 Challenges ………………………………………………………………………………………………….39
5.3 Future Works……………………………………………………………………………………………….39

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INTRODUCTION  

Text analysis is a field that has seen been growing rapidly over the years. The idea of text analysis came into being around the year 1950 (“Text Analytics: A Primer | GreenBook,”2017). Its main objective is to enable a descriptive view of structures and contents of a text document.

Text analysis is the act of understanding or deriving important information contained within a document or text. While structured data is generally being managed using a database system, text data is typically managed using a search engine owing to the fact that unstructured data is involved.

The percentage of unstructured data generated has been increasing rapidly as the years go by. The growth rate is about 55% to 65% each year as statistical records show (“Structured vs. Unstructured Data,” 2015). Analyzing text based on sentimental views is of utmost importance in this era.

To understand or predict the emotional balance or secret messages in a subjective context helps the data analysts in doing the desired job. Data analysis helps to obtain reasonable facts that can be used as a company’s marketing strategy. Responses from individuals can be used to determine when products are to be produced in bulk.

Using sentiment analysis can help in determining positive or negative views about a company (Saranya & Jayanthy, 2018), confidential and non-confidential messages to be seen by the public can also be determined through its use. Managing huge data set is quite difficult to handle, hence, the reason text classification comes into play.

Text classification is the act of classifying or arranging 4 a large amount of data generated into different or pre-defined categories as required. Without these classifications, it would be difficult to accumulate data. Proper arrangement of these data makes work easy. Despite making work easy, data classification requires a lot of work. 

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