6 NLP Datasets Beginners should use for their NLP Projects

NLP Datasets for NLP Projects - Feature Image
NLP Datasets for NLP Projects - Feature Image

Introduction

When beginners enter a new world of Machine Learning and Data Science, they are always advised to get hands-on experience as soon as possible. The best way is to make their own small projects which can help them to explore this domain in-depth. But for building such projects, you require datasets and ideas. In this article, we will help you with some publicly available, beginner-friendly NLP datasets along with some cool ideas on the NLP projects that can be done using them.

Before we go through the list of NLP datasets let us see what are some of the common NLP project ideas in general.

NLP Projects Ideas

Text Classification

Text Classification, also known as Text Tagging or Text Categorization, is the process of assigning tags or categories to text data based upon its content. With the help of NLP fundamentals, text classifiers can automatically analyze text data and then assign a set of pre-defined tags or categories. Example – Classifying emails, if it is spam or not based upon its content

Sentiment Analysis

One of the most widely known and implemented usage of natural language processing, Sentiment Analysis is a computational process for detecting the emotions/sentiments expressed in text data different individuals. Generally, sentiment analysis is helpful in finding sentiments of social media tweets, products, hotels, and movie reviews.

Chatbots

A chatbot is a software that mimics conversational attributes of human beings through auditory i.e. voice or textual methods. They are generally built with the aim of simulating human presence in conversations. These chatbots are frequently encountered on various websites, apps and even some well-known voice assistants like Alexa, Google Voice Assistant are kind of chatbot only.

Ad
Deep Learning Specialization on Coursera

Speech Recognition

Speech recognition is an interdisciplinary subfield of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.

Document Summarization

Document summarization is the process of creating a concise summary of data computationally, the aim of this summarization is to include the most important and relevant information of the text data. In addition to text, images and videos can also be summarized.

Automated Text Generation

Automated Text Generation is a way of predicting upcoming text in sentences with the help of natural language processing techniques and different learning techniques available in Artificial Intelligence. Some well-known examples are the autocomplete feature of Gmail, Google Search Engine, Mobile Keypads, etc.

Question Answering

A question answering implementation, usually a computer program, may construct its answers by querying a structured database of knowledge or information, usually a knowledge base. The advanced form of question answering systems can pull answers from an unstructured collection of natural language documents. These systems can also learn from the queries passed by users.

Subjectivity Prediction

In this prediction method, we can predict the subjective opinions of users. Generally, the ratings provided by users are a form of subjective opinions. Using various natural language processing techniques, we can predict subjective opinions based on other’s opinions.

Data Visualization

Data visualization is a graphical representation of data. It involves creating powerful visualizations that communicate relationships among the data to the users. For example, in NLP the most popular form of data visualization is word clouds.

NLP Datasets

Let us now see some of the useful, pubicly available NLP datasets along with some possible project ideas –

1) Sentiment Classification Data

This text classification data is hosted over Kaggle by the University of Michigan. The dataset consists of sentences obtained from social media, each sentence is annotated with ‘1’ representing ‘positive’ sentence and ‘0’ representing ‘negative’ sentence. The dataset is divided into two parts i.e. training data and testing data.

Ideas for NLP Projects

  • Sentiment Analysis
  • Prediction of Sentiments
  • Data Visualization of Sentiment Analysis.

Click here for data

2) Movie Reviews Data

This source consists of labeled movie reviews data, provided by Cornell University. There are different categories of datasets available, with main sections being sentiment polarity datasets and subjectivity datasets. Sentiment polarity datasets are useful in understanding the sentiments of reviews and Subjectivity datasets facilitate in knowing the ratings provided by individuals.

Ideas for NLP Projects

  • Sentiment Analysis of movie reviews using sentiment polarity datasets.
  • Sentiment Prediction of movie reviews.
  • Visualization of sentiment analysis.
  • Subjectivity Prediction (Rating prediction) of movie reviews.

Click here for data

3) Enron Dataset

The Enron dataset is a collection of emails generated by senior management of Enron. The dataset is arranged into different folders for ease of usage. This dataset contains large text data which is ideal for natural language processing projects.  Enron dataset is available in both unstructured and structured format. With the unstructured dataset, you need to apply your data preprocessing techniques for obtaining clean data.

Ideas for NLP Projects

  • Sentiment Analysis of Emails
  • Clustering of Email Conversation
  • Data Visualization

Click here for the unstructured dataset

Click here for the structured dataset

4) Amazon Reviews and Ratings Dataset

This amazon reviews dataset source is classic for projects related to natural language processing. Here you can get user reviews and user ratings of different products available on Amazon. These are large datasets that are required to be cleaned for better results.

Ideas for NLP Projects

  • Classifying reviews on the basis of sentiments.
  • Predicting the best and worst product by analyzing sentiments.
  • Visualizing the results obtained through reviews.
  • Using a rating dataset to find the most successful and most disappointing product.

Click here for dataset

5) Newsgroup Classification Dataset

The newsgroup dataset showcases another facet of the NLP project. This dataset is a collection of nearly 20,000 documents segregated into 20 different genres of news. Using the newsgroup dataset, we can explore how Natural Language Processing projects like text classification and text clustering are implemented. 

Ideas for NLP Projects

  • Predicting the genre/category of the document using the text clustering concept.
  • Classifying news data into various categories through text classification.
  • Sentiment Analysis of newsgroup documents of various categories.

Click here for dataset

6) IMDB Movie Review Dataset

This IMDB movie reviews dataset is hosted and provided by Stanford University. It consists of a huge dataset with highly polar reviews for both training and testing purposes. The source also consists of raw text which is beneficial for learning cleaning techniques of data. 

Ideas for NLP Projects

  • Predicting the polarity score of reviews.
  • Sentiment Classification of movie reviews.
  • Visualization of results of sentiment classification.

Conclusion

Before we end this article, I should mention an important point, always remember to read and understand the README file associated with the NLP datasets. This will help you in understanding what the dataset is about, what kind of data it consists of, what are its different parameters and in some cases, also how this dataset was created.

Along with these datasets, the main focus of the beginners should be to understand how the unstructured dataset is cleaned and structured, this is because the majority of tasks in NLP projects require you to handle unstructured data for obtaining the best possible results.

Like and Comment section (Community Members)

Create Your ML Profile!

Don't miss out to join exclusive Machine Learning community

Comments

No comments yet