Text Classification Using TF-IDF

Classifying reviews from multiple sources using NLP

Hi there, here’s another tutorial from my random dataset challenge series, where I build Machine Learning models on datasets hosted at the UCI Machine Learning Repository.

This series is a continuous effort to improve my data science skills by playing with different datasets; numerical, categorical and even, as you’ll see in this tutorial, text data. So if you’d like to check out some interesting techniques, keep reading and also take a peek at my previous articles.

Image for post

About the Dataset:

This tutorial uses the Sentiment Labelled Sentences Datasetwhich is a collection of user reviews and ratings pulled from 3 sites; Amazon, Yelp and IMDB. Each review is either labelled 0 for negative sentiment, or 1 for a positive sentiment related to the user’s experience of a product, film or place.

Acknowledgements —

This dataset was created for the paper, ‘From Group to Individual Labels using Deep Features’, Kotzias et. al,. KDD 2015.

_Term Frequency-Inverse Document Frequency : _TF-IDF determines how important a word is by weighing its frequency of occurence in the document and computing how often the same word occurs in other documents. If a word occurs many times in a particular document but not in others, then it might be highly relevant to that particular document and is therefore assigned more importance.

1) Data Preprocessing —

There are 3 separate datasets, one for each site and in the first gist below I’ve combined them into one, giant dataset. There are only 2 columns; ‘reviews’ and **‘ratings’. **Two of the reviews do not have a sentiment rating so I simply assigned the score 1 to them but feel free to drop those reviews if you’re implementing this tutorial.

The next preprocessing step involves cleaning up the reviews themselves using NLP techniques.

This is done to make sure that special characters and commonly occurring words are removed as they do not contain any useful information for the machine learning algorithm to learn.

Lemmatizing is also done here to convert the different inflected forms of a word to its base meaning (eg. happily, happiness -> happy). Again this is helpful to retain context-based information about the word without increasing the dimensionality of the TF-IDF matrix.

#sentiment-analysis #nlp #machine-learning #text-classification #data-science #data analysis

What is GEEK

Buddha Community

Text Classification Using TF-IDF
Daron  Moore

Daron Moore

1598404620

Hands-on Guide to Pattern - A Python Tool for Effective Text Processing and Data Mining

Text Processing mainly requires Natural Language Processing( NLP), which is processing the data in a useful way so that the machine can understand the Human Language with the help of an application or product. Using NLP we can derive some information from the textual data such as sentiment, polarity, etc. which are useful in creating text processing based applications.

Python provides different open-source libraries or modules which are built on top of NLTK and helps in text processing using NLP functions. Different libraries have different functionalities that are used on data to gain meaningful results. One such Library is Pattern.

Pattern is an open-source python library and performs different NLP tasks. It is mostly used for text processing due to various functionalities it provides. Other than text processing Pattern is used for Data Mining i.e we can extract data from various sources such as Twitter, Google, etc. using the data mining functions provided by Pattern.

In this article, we will try and cover the following points:

  • NLP Functionalities of Pattern
  • Data Mining Using Pattern

#developers corner #data mining #text analysis #text analytics #text classification #text dataset #text-based algorithm

Why Use WordPress? What Can You Do With WordPress?

Can you use WordPress for anything other than blogging? To your surprise, yes. WordPress is more than just a blogging tool, and it has helped thousands of websites and web applications to thrive. The use of WordPress powers around 40% of online projects, and today in our blog, we would visit some amazing uses of WordPress other than blogging.
What Is The Use Of WordPress?

WordPress is the most popular website platform in the world. It is the first choice of businesses that want to set a feature-rich and dynamic Content Management System. So, if you ask what WordPress is used for, the answer is – everything. It is a super-flexible, feature-rich and secure platform that offers everything to build unique websites and applications. Let’s start knowing them:

1. Multiple Websites Under A Single Installation
WordPress Multisite allows you to develop multiple sites from a single WordPress installation. You can download WordPress and start building websites you want to launch under a single server. Literally speaking, you can handle hundreds of sites from one single dashboard, which now needs applause.
It is a highly efficient platform that allows you to easily run several websites under the same login credentials. One of the best things about WordPress is the themes it has to offer. You can simply download them and plugin for various sites and save space on sites without losing their speed.

2. WordPress Social Network
WordPress can be used for high-end projects such as Social Media Network. If you don’t have the money and patience to hire a coder and invest months in building a feature-rich social media site, go for WordPress. It is one of the most amazing uses of WordPress. Its stunning CMS is unbeatable. And you can build sites as good as Facebook or Reddit etc. It can just make the process a lot easier.
To set up a social media network, you would have to download a WordPress Plugin called BuddyPress. It would allow you to connect a community page with ease and would provide all the necessary features of a community or social media. It has direct messaging, activity stream, user groups, extended profiles, and so much more. You just have to download and configure it.
If BuddyPress doesn’t meet all your needs, don’t give up on your dreams. You can try out WP Symposium or PeepSo. There are also several themes you can use to build a social network.

3. Create A Forum For Your Brand’s Community
Communities are very important for your business. They help you stay in constant connection with your users and consumers. And allow you to turn them into a loyal customer base. Meanwhile, there are many good technologies that can be used for building a community page – the good old WordPress is still the best.
It is the best community development technology. If you want to build your online community, you need to consider all the amazing features you get with WordPress. Plugins such as BB Press is an open-source, template-driven PHP/ MySQL forum software. It is very simple and doesn’t hamper the experience of the website.
Other tools such as wpFoRo and Asgaros Forum are equally good for creating a community blog. They are lightweight tools that are easy to manage and integrate with your WordPress site easily. However, there is only one tiny problem; you need to have some technical knowledge to build a WordPress Community blog page.

4. Shortcodes
Since we gave you a problem in the previous section, we would also give you a perfect solution for it. You might not know to code, but you have shortcodes. Shortcodes help you execute functions without having to code. It is an easy way to build an amazing website, add new features, customize plugins easily. They are short lines of code, and rather than memorizing multiple lines; you can have zero technical knowledge and start building a feature-rich website or application.
There are also plugins like Shortcoder, Shortcodes Ultimate, and the Basics available on WordPress that can be used, and you would not even have to remember the shortcodes.

5. Build Online Stores
If you still think about why to use WordPress, use it to build an online store. You can start selling your goods online and start selling. It is an affordable technology that helps you build a feature-rich eCommerce store with WordPress.
WooCommerce is an extension of WordPress and is one of the most used eCommerce solutions. WooCommerce holds a 28% share of the global market and is one of the best ways to set up an online store. It allows you to build user-friendly and professional online stores and has thousands of free and paid extensions. Moreover as an open-source platform, and you don’t have to pay for the license.
Apart from WooCommerce, there are Easy Digital Downloads, iThemes Exchange, Shopify eCommerce plugin, and so much more available.

6. Security Features
WordPress takes security very seriously. It offers tons of external solutions that help you in safeguarding your WordPress site. While there is no way to ensure 100% security, it provides regular updates with security patches and provides several plugins to help with backups, two-factor authorization, and more.
By choosing hosting providers like WP Engine, you can improve the security of the website. It helps in threat detection, manage patching and updates, and internal security audits for the customers, and so much more.

Read More

#use of wordpress #use wordpress for business website #use wordpress for website #what is use of wordpress #why use wordpress #why use wordpress to build a website

I am Developer

1597475640

Laravel 7 Full Text Search MySQL

Here, I will show you how to create full text search in laravel app. You just follow the below easy steps and create full text search with mysql db in laravel.

Laravel 7 Full Text Search Mysql

Let’s start laravel full-text search implementation in laravel 7, 6 versions:

  1. Step 1: Install Laravel New App
  2. Step 2: Configuration DB .evn file
  3. Step 3: Run Migration
  4. Step 4: Install Full Text Search Package
  5. Step 5: Add Fake Records in DB
  6. Step 6: Add Routes,
  7. Step 7: Create Controller
  8. Step 8: Create Blade View
  9. Step 9: Start Development Server

https://www.tutsmake.com/laravel-full-text-search-tutorial/

#laravel full text search mysql #laravel full text search query #mysql full text search in laravel #full text search in laravel 6 #full text search in laravel 7 #using full text search in laravel

Text Classification Using TF-IDF

Classifying reviews from multiple sources using NLP

Hi there, here’s another tutorial from my random dataset challenge series, where I build Machine Learning models on datasets hosted at the UCI Machine Learning Repository.

This series is a continuous effort to improve my data science skills by playing with different datasets; numerical, categorical and even, as you’ll see in this tutorial, text data. So if you’d like to check out some interesting techniques, keep reading and also take a peek at my previous articles.

Image for post

About the Dataset:

This tutorial uses the Sentiment Labelled Sentences Datasetwhich is a collection of user reviews and ratings pulled from 3 sites; Amazon, Yelp and IMDB. Each review is either labelled 0 for negative sentiment, or 1 for a positive sentiment related to the user’s experience of a product, film or place.

Acknowledgements —

This dataset was created for the paper, ‘From Group to Individual Labels using Deep Features’, Kotzias et. al,. KDD 2015.

_Term Frequency-Inverse Document Frequency : _TF-IDF determines how important a word is by weighing its frequency of occurence in the document and computing how often the same word occurs in other documents. If a word occurs many times in a particular document but not in others, then it might be highly relevant to that particular document and is therefore assigned more importance.

1) Data Preprocessing —

There are 3 separate datasets, one for each site and in the first gist below I’ve combined them into one, giant dataset. There are only 2 columns; ‘reviews’ and **‘ratings’. **Two of the reviews do not have a sentiment rating so I simply assigned the score 1 to them but feel free to drop those reviews if you’re implementing this tutorial.

The next preprocessing step involves cleaning up the reviews themselves using NLP techniques.

This is done to make sure that special characters and commonly occurring words are removed as they do not contain any useful information for the machine learning algorithm to learn.

Lemmatizing is also done here to convert the different inflected forms of a word to its base meaning (eg. happily, happiness -> happy). Again this is helpful to retain context-based information about the word without increasing the dimensionality of the TF-IDF matrix.

#sentiment-analysis #nlp #machine-learning #text-classification #data-science #data analysis

Noah  Rowe

Noah Rowe

1596681180

Multi Class Text Classification With Deep Learning Using BERT

Most of the researchers submit their research papers to academic conference because its a faster way of making the results available. Finding and selecting a suitable conference has always been challenging especially for young researchers.

However, based on the previous conferences proceeding data, the researchers can increase their chances of paper acceptance and publication. We will try to solve this text classification problem with deep learning using BERT.

Almost all the code were taken from this tutorial, the only difference is the data.

The Data

The dataset contains 2,507 research paper titles, and have been manually classified into 5 categories (i.e. conferences) that can be downloaded from here.

Explore and Preprocess

import torch
	from tqdm.notebook import tqdm

	from transformers import BertTokenizer
	from torch.utils.data import TensorDataset

	from transformers import BertForSequenceClassification

	df = pd.read_csv('data/title_conference.csv')
	df.head()
view raw
conf_explore.py hosted with ❤ by GitHub

conf_explore.py

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Table 1

df['Conference'].value_counts()

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Figure 1

You may have noticed that our classes are imbalanced, and we will address this later on.

#machine-learning #nlp #document-classification #nlp-tutorial #text-classification #deep learning