.

Reading the academic literature Text Analytics seems difficult. However, applying it in practice has shown us that Text Classification is much easier than it looks. Most of the Classifiers consist of only a few lines of code.In this three-part blog series we will examine the three well-known Classifiers; the Naive Bayes, Maximum Entropy and Support Vector Machines. From the introductionary blog we know that the Naive Bayes Classifier is based on the bag-of-words model.

With the bag-of-words model we check which word of the text-document appears in a positive-words-list or a negative-words-list. If the word appears in a positive-words-list the total score of the text is updated with +1 and vice versa. If at the end the total score is positive, the text is classified as positive and if it is negative, the text is classified as negative. Simple enough!

With the Naive Bayes model, we do not take only a small set of positive and negative words into account, but all words the NB Classifier was trained with, i.e. all words presents in the training set. If a word has not appeared in the training set, we have no data available and apply Laplacian smoothing (use 1 instead of the conditional probability of the word). The probability a document belongs to a class * C* is given by the class probability

Here *count(d_i, C) *is the number of occurences of word *d_i* in class *C* , *V_C* is the total number of words in class *C* and *n* is the number of words in the document we are currently classifying.

*V_C* does not change (unless the training set is expanded), so it can be placed outside of the product:

In theory we want a training set as large as possible, since that will increase the accuracy. In practice this results in large numbers for *V_C* and *n*. For example, for our training set with 5000 reviews we got

Taking the n-th power of such a large number, will definitely result in computational problems, so we should normalize it. We can divide it by a number so that it becomes a number close to 1. In our case, this number is 100.000 and this normalization results in:

However, if the number of words in the document is large, this can still lead to computational problems:

`>>> 4.59**500` `Traceback (most recent call last):` ` ` `File` `"<stdin>"` `, line 1,` `in` `<module>` `OverflowError: (34,` `'Result too large'` `)` `>>>` |

In Python there are a few modules which can handle large number (like Decimal), but a better solution would be to take the logarithm. This will not affect the outcome of the classification process; if a document has the highest probability for a specific class, the logarithm of the probabilities will also be the highest for that class.

This results in:

With this information it is easy to implement a Naive Bayes Classifier algorithm.

Our training set consists in the form of a list of tuples, where each tuple contains two elements; the tokenized text and the label.

Check out the rest of the first blog-post about the Naive Bayes Classifier

- 11 data science skills for machine learning and AI
- Get started on AWS with this developer tutorial for beginners
- Microsoft, Zoom gain UCaaS market share as Cisco loses
- Develop 5G ecosystems for connectivity in the remote work era
- Choose between Microsoft Teams vs. Zoom for conference needs
- How to prepare networks for the return to office
- Qlik keeps focus on real-time, actionable analytics
- Data scientist job outlook in post-pandemic world
- 10 big data challenges and how to address them
- 6 essential big data best practices for businesses
- Hadoop vs. Spark: Comparing the two big data frameworks
- With accelerated digital transformation, less is more
- 4 IoT connectivity challenges and strategies to tackle them

Posted 10 May 2021

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central