News text Analysis using Text Summarization and Sentiment Analysis based on NLP

Abstract

Every day, at least 2.5 quintillion bytes of data are generated worldwide. This results in information explosion. Excessive information about a subject makes it difficult to focus on the most important concepts and findings. As a result, it becomes challenging for data analysts to determine which data is correct and which data is unnecessary for a given task. Natural Language Processing (NLP) based text summarization is an effective solution to this problem. Text summarization helps to reduce the size of a data or text while retaining the information. At the same time, it is highly difficult to manually summarize lengthy text documents. The primary goal of the proposed text summarization model is to highlight and present consumers with the most pertinent information from the provided text data. Using text summarization and NLTK, this study attempts to propose a text sentiment analysis on news material.