Advanced Applications on Bilingual Document Analysis and Processing Systems

Abstract

Today, rapid digitization requires efficient bilingual non-image and image document classification systems. Although many bilingual NLP and image-based systems provide solutions for real-world problems, they primarily focus on text extraction, identification, and recognition tasks with limited document types. This article discusses a journey of these systems and provides an overview of their methods, feature extraction techniques, document sets, classifiers, and accuracy for English-Hindi and other language pairs. The gaps found lead toward the idea of a generic and integrated bilingual English-Hindi document classification system, which classifies heterogeneous documents using a dual class feeder and two character corpora. Its non-image and image modules include pre- and post-processing stages and pre-and post-segmentation stages to classify documents into predefined classes. This article discusses many real-life applications on societal and commercial issues. The analytical results show important findings of existing and proposed systems.

Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-based Approach

“…Automated sentiment analysis or opinion mining [1]- [3], [5]- [6], [8]- [17]is a prominent sub-field of the Natural Language Processing (NLP) and Machine Learning (ML), which interprets, extracts, summarizes and classifies the text [4], emotions and images [7] [18] in the subjective review data. It is also known as the subjective analysis to classify the text according to the polarity and orientation of the opinions.…”