nlp projects in python

Top20 Machine Learning Projects on NLP

1.Resume Screening with Python

Companies often receive thousands of resumes for each job posting and employ dedicated screening officers to screen qualified candidates. In this article, I will introduce you to a machine learning project on Resume Screening with Python programming language.

What is Resume Screening?

Hiring the right talent is a challenge for all businesses. This challenge is magnified by the high volume of applicants if the business is labour-intensive, growing, and facing high attrition rates.

An example of such a business is that IT departments are short of growing markets. In a typical service organization, professionals with a variety of technical skills and business domain expertise are hired and assigned to projects to resolve customer issues. This task of selecting the best talent among many others is known as Resume Screening.

Typically, large companies do not have enough time to open each CV, so they use machine learning algorithms for the Resume Screening task.

Machine Learning Project on Resume Screening with Python

In this section, I will take you through a Machine Learning project on Resume Screening with Python programming language. I will start this task by importing the necessary Python libraries and the dataset: Link

2.Named Entity Recognition with Python

Named Entity means anything that is a real-world object such as a person, a place, any organisation, any product which has a name. For example – “My name is Aman, and I and a Machine Learning Trainer”. In this sentence the name “Aman”, the field or subject “Machine Learning” and the profession “Trainer” are named entities.

In Machine Learning Named Entity Recognition (NER) is a task of Natural Language Processing to identify the named entities in a certain piece of text.

Have you ever used software known as Grammarly? It identifies all the incorrect spellings and punctuations in the text and corrects it. But it does not do anything with the named entities, as it is also using the same technique. In this article, I will take you through the task of Named Entity Recognition (NER) with Machine Learning.

Loading the Data for Named Entity Recognition (NER)

The dataset, that I will use for this task can be easily downloaded from here. Now the first thing I will fo is to load the data and have a look at it to know what I am working with. So let’s simply import the pandas library and load the data: Link

 

3.Sentiment Analysis with Python

In Machine Learning, Sentiment analysis refers to the application of natural language processing, computational linguistics, and text analysis to identify and classify subjective opinions in source documents. In this article, I will introduce you to a machine learning project on sentiment analysis with the Python programming language.

In Machine Learning, Sentiment analysis refers to the application of natural language processing, computational linguistics, and text analysis to identify and classify subjective opinions in source documents. In this article, I will introduce you to a machine learning project on sentiment analysis with the Python programming language.

In sentiment analysis, the main task is to identify opinion words, which is very important. Opinion words are dominant indicators of feelings, especially adjectives, adverbs, and verbs, for example: “I love this camera. It’s amazing!”

Opinion words are also known as polarity words, sentiment words, opinion lexicon, or opinion words, which can generally be divided into two types: positive words, for example, wonderful. , elegant, astonishing; and negative words, eg horrible, disgusting, poor.

Machine Learning Project on Sentiment Analysis with Python

Now in this section, I will take you through a Machine Learning project on sentiment analysis with Python programming language. Let’s start by importing all the necessary Python libraries and the dataset: Link

4.Keyword Extraction with Python

In this article, I will take you through a Machine Learning project on Keyword Extraction with Python programming language. In machine learning, Keyword extraction is a task of Natural Language Processing.

What is Keyword Extraction?

Keyword extraction is defined as the task of Natural language processing that automatically identifies a set of terms to describe the subject of the text. This is an important method in information retrieval (IR) systems: keywords simplify and speed up research. Keyword extraction can be used to reduce text dimensionality for further text analysis (subject modeling text classification).

The task of keyword extraction can be used in automatically indexing data, summarizing text, or generating tag clouds with the most representative keywords.

Machine Learning Project on Keyword Extraction with Python

Now, in this section, I will take you through a Machine Learning project on Keyword Extraction with Python programming language. I will start by importing the necessary libraries and the dataset:  Link

5.Spelling Correction Model with Python

In this article, I will take you through how to write a program to correct spellings with Python programming language. For this task, I will use an NLP library in Python known as TextBlob.

What is TextBlob?

TextBlob is a Python library for processing text data. It provides a simple API for delving into common natural language processing tasks such as tagging part of speech, extracting nominal sentences, analyzing feelings, classifying, translating, and more.

• Noun phrase extraction
• Part-of-speech tagging
• Sentiment analysis
• Classification
• Tokenization
• Word and phrase frequencies
• Parsing
• n-grams
• Word inflexion and lemmatization
• Spelling correction
• Add new models or languages through extensions
• WordNet integration

You can simply install the TextBlob library in your systems by writing a pip command; pip install textblob.  Link

6.Keyboard Autocorrection Model

In this article, I will take you through how we can predict the US presidential elections with Python. Here, I will not train any machine learning model. I will analyze the sentiments of people for the candidates and then at the end, I will conclude based on the most number of positive and negative tweets against the candidates.

The datasets that I am using in this task to predict the US Elections are collected from twitter by the official twitter handles of Donald Trump and Joe Biden. You can download the datasets that I am using from here.

7.Election Results Prediction by analyzing Tweets

In this article, I will take you through how we can predict the US presidential elections with Python. Here, I will not train any machine learning model. I will analyze the sentiments of people for the candidates and then at the end, I will conclude based on the most number of positive and negative tweets against the candidates.

The datasets that I am using in this task to predict the US Elections are collected from twitter by the official twitter handles of Donald Trump and Joe Biden. You can download the datasets that I am using from here.

8.NLP for Other languages

Natural Language Processing (NLP) is a great task in Machine Learning to work with languages. However, you must have seen everyone working with only in the English language while working on a task of NLP. So what about other languages that we have. In this article, I will take you through NLP for other Languages with Machine Learning.

Everyone knows India is a very diverse country and a hotbed of many languages, but did you know India speaks 780 languages. It’s time to move beyond English when it comes to NLP. This article is intended for those who know a little about NLP and want to start using NLP for other languages.

NLP for Other Languages

Before we get into the task of NLP for other languages, let’s take a look at some essential concepts and recent achievements in NLP. NLP helps computers understand human language. Text classification, information extraction, semantic analysis, question answering, text synthesis, machine translation and chatbots are some applications of NLP.

For computers to understand human language, we must first represent words in digital form. Thse digitally represented words can then be used by machine learning models to perform any NLP task. Traditionally, methods like One Hot Encoding, TF-IDF Representation have been used to describe the text as numbers. But traditional methods have resulted in sparse representation by not grasping the meaning of the word.

Neural Word Embeddings then came to the rescue by solving the problems in traditional ways. Word2Vec and GloVe are the two most commonly used word embedding elements. These methods have resulted in dense representations where words with similar meanings will have similar representations. A significant weakness of this method is that the words are considered to have only one meaning. But we know that a word can have many meanings depending on the context in which it is used.

NLP has leapt forward in the modern family of language models. The incorporation of words is no longer independent of the context. The same word can have multiple digital representations depending on the context in which it is used. BERT, Elmo, ULMFit, GPT-2 are currently popular language models. The last generation is so good and some people see it as dangerous. The information written by these linguistic models was even deemed as credible as the New York Times by readers.

NLP for Other Languages in Action

I will now get into the task of NLP for other languages ​​by getting the integration of words for Indian languages. The digital representation of words plays a role in any NLP task. We are going to use the iNLTK (Natural Language Toolkit for Indic Languages) library. You can easily install the iNLTK library by using the pip command: pip install inltk.

The Languages provided by inltk library are given below:

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Using iNLTK we can quickly get the embedding vectors for the sentences written in Indian languages. Below is an example that shows how to get the integration vectors for a sentence written in Hindi. The given sentence will be divided into tokens, and each token will be represented using a vector. A token can be a word or a subword. Since tokens can be subwords, we can also get meaningful vector representation for rare words.

Let’s see how to use inltk library for NLP for other languages: Link

9.Text Classification using Deep Learning

In this article, I will introduce you to a text classification model with TensorFlow on movie reviews as positive or negative using the text of the reviews. This is a binary classification problem, which is an important and widely applicable type of machine learning problem.

Text Classification with TensorFlow

I’ll walk you through the basic application of transfer learning with TensorFlow Hub and Keras. I will be using the IMDB dataset which contains the text of 50,000 movie reviews from the internet movie database. These are divided into 25,000 assessments for training and 25,000 assessments for testing. The training and test sets are balanced in a way that they contain an equal number of positive and negative reviews.

Now, let’s get started with this task of text classification with TensorFlow by importing some necessary libraries: Link

10.Summarize Text with Machine Learning

Text Summarization involves condensing a piece of text into a shorter version, reducing the size of the original text while preserving key information and the meaning of the content. Since manual text synthesis is a long and generally laborious task, task automation is gaining in popularity and therefore a strong motivation for academic research. In this article, I will take you through the task of Natural Language Processing to summarize text with Machine Learning.

In Machine Learning, there are important applications for text summarization in various Natural Language Processing related tasks such as text classification, answering questions, legal text synthesis, news synthesis, and headline generation which can be achieved with Machine Learning. The intention to summarize a text is to create an accurate and fluid summary containing only the main points described in the document.

Types of Approaches to Summarize Text

Before I dive into showing you how we can summarize text using machine learning and python, it is important to understand what are the types of text summarization to understand how the process works, so that we can use logic while using machine learning techniques to summarize the text.

Generally, Text Summarization is classified into two main types: Extraction Approach and Abstraction Approach. Now let’s go through both these approaches before we dive into the coding part.

The Extractive Approach

The Extractive approach takes sentences directly from the document according to a scoring function to form a cohesive summary. This method works by identifying the important sections of the text cropping and assembling parts of the content to produce a condensed version.

The Abstractive Approach

The Abstraction approach aims to produce a summary by interpreting the text using advanced natural language techniques to generate a new, shorter text – parts of which may not appear in the original document, which conveys the most information.

In this article, I will be using the extractive approach to summarize text using Machine Learning and Python. I will use the TextRank algorithm which is an extractive and unsupervised machine learning algorithm for text summarization.

Summarize Text with Machine Learning

So now, I hope you know what text summarization is and how it works. Now, without wasting any time let’s see how we can summarize text using machine learning. The dataset that I will use in this task can be downloaded from here. Now, let’s import the necessary packages that we need to get started with the task:Link

11.Hate Speech Detection Model

The term hate speech is understood as any type of verbal, written or behavioural communication that attacks or uses derogatory or discriminatory language against a person or group based on what they are, in other words, based on their religion, ethnicity, nationality, race, colour, ancestry, sex or another identity factor. In this article, I will take you through a hate speech detection model with Machine Learning and Python.

Hate Speech Detection is generally a task of sentiment classification. So for training, a model that can classify hate speech from a certain piece of text can be achieved by training it on a data that is generally used to classify sentiments. So for the task of hate speech detection model, I will use the Twitter data.

Hate Speech Detection is generally a task of sentiment classification. So for training, a model that can classify hate speech from a certain piece of text can be achieved by training it on a data that is generally used to classify sentiments. So for the task of hate speech detection model, I will use the Twitter data. Link

12.Keyword Research with Python

Google Trends is a keyword research tool that helps the researchers, bloggers, digital marketers and some more people in the digital industry to find how often a keyword is entered into Google search engine over a given period. Google Trends is used for keyword research mostly is writing articles on hot topics. In this article, I’ll walk you through how to perform keyword research with python to find the hottest topics and keywords.

I will use the Google API to access Google trends which can be done by using the pytrends library in python. Python being a general-purpose programming language provides libraries and packages for almost every task. pytrends can be easily installed by using the pip command – pip install pytrends. I hope that you have easily installed this package, now let’s start with the task of keyword research with python.

Keyword Research with Python

You need to log in to Google first because, after all, we ask Google Trends for trending topics. For that, we need to import the method called TrendReq from the pytrends.request method: Link

13. Whatsapp Group Chat Analysis

So I am a part of a WhatsApp group named as “Data Science Community”, recently I thought to explore the chat of this group and do some analysis on it. So, here in this article, I will take you through a WhatsApp group chat Analysis with Data Science.

If you don’t know how to extract the messages from any chat then just open any chat click on the 3 dots above, select more and then select explore chat, and share it with any means, most preferable your email.

The chat you will get at the end does not need any cleaning and preparation it can be used directly for the task. Now let’s start with this WhatsApp group chat analysis, I will simply import the required packages and get started with the task:Link

14.Next Word Prediction Model

Most of the keyboards in smartphones give next word prediction features; google also uses next word prediction based on our browsing history. So a preloaded data is also stored in the keyboard function of our smartphones to predict the next word correctly. In this article, I will train a Deep Learning model for next word prediction using Python. I will use the Tensorflow and Keras library in Python for next word prediction model.

Next Word Prediction Model

To start with our next word prediction model, let’s import some all the libraries we need for this task: Link

15.NLP for Whatsapp Chats

Natural Language Processing or NLP is a field of Artificial Intelligence which focuses on enabling the systems for understanding and processing the human languages. In this article, I will use NLP to analyze my WhatsApp Chats. For some privacy reasons, I will use Person 1, Person 2 and so on in my WhatsApp Chats.

Get The Whatsapp Data for NLP

If you have never exported your whatsapp chats before, don’t worry it’s very easy. For NLP of WhatsApp chats, you need to extract the whatsapp chats from your smartphone. You just need to open any chat in your whatsapp then select the export chat option. The text file you will get as a return will look like this: Link

16.Twitter Sentiment Analysis

Twitter Sentiment Analysis is the process of computationally identifying and categorizing tweets expressed in a piece of text, especially in order to determine whether the writer’s attitude towards a particular topic, product, etc. is positive, negative, or neutral.

In this Article I will do twitter sentiment analysis with Natural Language Processing using the nltk library with python.Link

17.SMS Spam Detection Model

This Article is based on SMS Spam detection classification with Machine Learning. I will be using the multinomial Naive Bayes implementation.

This particular classifier is suitable for classification with discrete features (such as in our case, word counts for text classification). It takes in integer word counts as its input.

On the other hand Gaussian Naive Bayes is better suited for continuous data as it assumes that the input data has a Gaussian(normal) distribution. Link

18.Movie Reviews Sentiment analysis

In this Machine Learning Project, we’ll build binary classification that puts movie reviews texts into one of two categories — negative or positive sentiment. We’re going to have a brief look at the Bayes theorem and relax its requirements using the Naive assumption. Link

19.Amazon Product Reviews Sentiment Analysis

Product reviews are becoming more important with the evolution of traditional brick and mortar retail stores to online shopping.

Consumers are posting reviews directly on product pages in real time. With the vast amount of consumer reviews, this creates an opportunity to see how the market reacts to a specific product.

We will be attempting to see if we can predict the sentiment of a product review using python and machine learning.

Let’s Import the necessary Modules and take a look at the data:

You can download this dataset from here.