Impact of Online Education and Sentiment Analysis from Twitter Data using Topic Modeling Algorithms

During a pandemic, all industries suffer greatly, and every sector of the world suffers in some way, including the education sector. Internet expressions reflect users' feelings about a product or service. The polarity of information in source data toward a subject under investigation is determined by sentiment analysis processes. The goal of this study is to examine social media expressions about online teaching and learning, as online education will become a part of everyday life in the future. We collected data from Twitter using keywords related to online education and Google form from engineering undergraduate students for prototype implementation. This analysis will assist teachers, parents, and the student community in understanding the benefits and drawbacks of the education industry, allowing for further improvement in educational outcomes. We used aspect-based sentiment analysis and topic modeling to determine sentiment polarity and important topics for education sector stakeholders. To begin, we used TextBlob Python package to determine sentiment polarity, and Bag of Words, LDA and LSA model for discovering topics. After modeling topics from the collected data, topic Coherence is used to assess the degree of semantic similarity between high-scoring words in the topic. The word cloud and LDAvis are used to visualize data. The experimental results are promising and it will assist education stakeholders in addressing the concerns that have been identified as social media expressions to work on

Artificial Generation of Realistic Voices

In this paper, we propose an end-to-end text-to-speech system deployment wherein a user feeds input text data which gets synthesized, variated, and altered into artificial voice at the output end. To create a text-to-speech model, that is, a model capable of generating speech with the help of trained datasets. It follows a process which organizes the entire function to present the output sequence in three parts. These three parts are Speaker Encoder, Synthesizer, and Vocoder. Subsequently, using datasets, the model accomplishes generation of voice with prior training and maintains the naturalness of speech throughout. For naturalness of speech we implement a zero-shot adaption technique. The primary capability of the model is to provide the ability of regeneration of voice, which has a variety of applications in the advancement of the domain of speech synthesis. With the help of speaker encoder, our model synthesizes user generated voice if the user wants the output trained on his/her voice which is feeded through the mic, present in GUI. Regeneration capabilities lie within the domain Voice Regeneration which generates similar voice waveforms for any text

A scene perception system for visually impaired based on object detection and classification using CNN

In this paper we have developed a system for visually impaired people using OCR and machine learning. Optical Character Recognition is an automated data entry tool. To convert handwritten, typed or printed text into data that can be edited on a computer, OCR software is used. The paper documents are scanned on simple systems with an image scanner. Then, the OCR program looks at the image and compares letter shapes to stored letter images. OCR in English has evolved over the course of half a century to a point that we have established application that can seamlessly recognize English text. This may not be the case for Indian languages, as they are much more complex in structure and computation compared to English. Therefore, creating an OCR that can execute Indian languages as suitably as it does for English becomes a must. Devanagari is one of the Indian languages spoken by more than 70% of people in Maharashtra, so some attention should be given to studying ancient scripts and literature. The main goal is to develop a Devanagari character recognition system that can be implemented in the Devanagari script to recognize different characters, as well as some words