Exploring sentiment analysis in handwritten and E-text documents using advanced machine learning techniques: a novel approach - Rayees Ahamad; Kamta Nath Mishra (Journal of Big Data)
Abstract Traditionally, many people still wish to write on pen and paper. However, it has some drawbacks like accessing and storing physical documents efficiently, searching through them, and sharing them efficiently. Handwriting-to-text recognition classifies an individual’s handwriting and converts it into digital form. However, Handwriting Image to E-Text Conversion (HTC) removes all of the mentioned problems as it is easier to store, retrieve, and use the text as and when required. Emotions are a basic and very important aspect of anyone’s life. To understand this important aspect of an individual’s life, we have to detect emotions using affect data like text, voice, images, etc. This research work investigates the application of machine learning and deep learning methods in performing sentiment analysis on both handwritten and E-text statements. The primary objective of this research work is to distinguish the sentiment polarity and categorize it as positive, negative, or neutral while identifying emotion types such as happiness, sadness, surprise, fear, anger, disgust, and contempt. The study employs sophisticated methodologies to analyze handwritten image documents and E-text statements to provide a comprehensive understanding of sentiment nuances in diverse forms of communication. The authors proposed the Exploration of Sentiment Insights in Handwritten and E-text through Advanced Machine Learning (ESIHE_AML) algorithms-based model that finds the sentiment polarity and emotion types of handwritten text as well as E-Text. The results of the proposed model are described using various machine learning and deep learning-based approaches. Further, it significantly contributes to the advancements in sentiment analysis techniques and offers valuable insights into the emotional content present in both traditional and handwritten text formats. The proposed model shows higher accuracy (more than 90% in all cases) on standard bench mark datasets of Twitter, Kaggle, IAM, and Amazon reviews etc. It may further be increased by employing a hybrid approach of intelligent algorithms. This study highlights the adaptability of the ESIHE_AML algorithms-based model for analyzing sentiments in digital communication systems of the modern era.