Text Analysis Tool

Abstract

The automated analysis of electronic text is referred to as “text processing”. The amount of online textual data is increasing, and automatic text processing techniques have the potential to be tremendously beneficial because they can gather more meaningful information faster. Features like text summarization, language translation, emotion classifier and headline generation of news articles are some of the popular ways of text processing. The fundamental goal of text summarization is to extract the most important information from a text and deliver it in a concise and legible form. The practice of transforming written text from one language into another such that it may be easily understood is known as language translation. Emotion classifier analyses the emotions and categorizes the text into various emotions. Headline generation is the process of obtaining the headlines from various news articles. This paper is about developing a text processing tool based on the concepts of Machine Learning and Natural Language Processing. Implementation of this tool allows for the automation of text processing. This tool aims to improve productivity and efficiency of processed data by faster generation of precise and meaningful data to all the providers across all platforms.