An Opinion Mining of Text in COVID-19 Issues along with Comparative Study in ML, BERT & RNN

The global world is crossing a pandemic situation where this is a catastrophic outbreak of Respiratory Syndrome recognized as COVID-19. This is a global threat all over the 212 countries that people every day meet with mighty situations. On the contrary, thousands of infected people live rich in mountains. Mental health is also affected by this worldwide coronavirus situation. Due to this situation online sources made a communicative place that common people shares their opinion in any agenda. Such as affected news related positive and negative, financial issues, country and family crisis, lack of import and export earning system etc. different kinds of circumstances are recent trendy news in anywhere. Thus, vast amounts of text are produced within moments therefore, in subcontinent areas the same as situation in other countries and peoples opinion of text and situation also same but the language is different. This article has proposed some specific inputs along with Bangla text comments from individual sources which can assure the goal of illustration that machine learning outcome capable of building an assistive system. Opinion mining assistive system can be impactful in all language preferences possible. To the best of our knowledge, the article predicted the Bangla input text on COVID-19 issues proposed ML algorithms and deep learning models analysis also check the future reachability with a comparative analysis. Comparative analysis states a report on text prediction accuracy is 91% along with ML algorithms and 79% along with Deep Learning Models.

[1]  Wasifa Chowdhury,et al.  Performing sentiment analysis in Bangla microblog posts , 2014, 2014 International Conference on Informatics, Electronics & Vision (ICIEV).

[2]  Dirk Neumann,et al.  Automated news reading: Stock price prediction based on financial news using context-capturing features , 2013, Decis. Support Syst..

[3]  Leon Derczynski,et al.  Complementarity, F-score, and NLP Evaluation , 2016, LREC.

[4]  Md. Jahid Hasan,et al.  Cricket Sentiment Analysis from Bangla Text Using Recurrent Neural Network with Long Short Term Memory Model , 2019, 2019 International Conference on Bangla Speech and Language Processing (ICBSLP).

[5]  Stuart Soroka,et al.  Affective News: The Automated Coding of Sentiment in Political Texts , 2012 .

[6]  Steven Euijong Whang,et al.  A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective , 2018, IEEE Transactions on Knowledge and Data Engineering.

[7]  S. Klisarova-Belcheva,et al.  Effects of COVID-19 Pandemic on University Students' Learning , 2021, Inf..

[8]  Ahmad Ali,et al.  Sentiment Analysis on Twitter Data using KNN and SVM , 2017 .

[9]  Anjir Ahmed Chowdhury,et al.  Sentiment Analysis of COVID-19 Vaccination from Survey Responses in Bangladesh. , 2021 .

[10]  Wei Xu,et al.  Bidirectional LSTM-CRF Models for Sequence Tagging , 2015, ArXiv.

[11]  Omer Levy,et al.  What Does BERT Look at? An Analysis of BERT’s Attention , 2019, BlackboxNLP@ACL.

[12]  Amit Kumar Das,et al.  An Automated System of Sentiment Analysis from Bangla Text using Supervised Learning Techniques , 2019, 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS).

[13]  Pei-Chann Chang,et al.  Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news , 2013, Knowl. Based Syst..

[14]  J. A. Miranda.  The Oxford Handbook of World History , 2014 .

[15]  Sharun Akter Khushbu,et al.  COVID-19 in Bangladesh: A Deeper Outlook into The Forecast with Prediction of Upcoming Per Day Cases Using Time Series , 2020, Procedia Computer Science.

[16]  M. Nissim,et al.  Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , 2013 .

[17]  Yi Wang,et al.  Mental health problems and social media exposure during COVID-19 outbreak , 2020, PloS one.

[18]  Christopher Ré,et al.  Understanding Tables in Context Using Standard NLP Toolkits , 2013, ACL.

[19]  Anupama J Nair,et al.  Comparative study of Twitter Sentiment On COVID - 19 Tweets , 2021, 2021 5th International Conference on Computing Methodologies and Communication (ICCMC).

[20]  R. C. Tripathi,et al.  A Review towards the Sentiment Analysis Techniques for the Analysis of Twitter Data , 2019 .

[21]  Md. Habibul Alam,et al.  Sentiment analysis for Bangla sentences using convolutional neural network , 2017, 2017 20th International Conference of Computer and Information Technology (ICCIT).

[22]  Ram Mohana Reddy Guddeti,et al.  NLP based sentiment analysis on Twitter data using ensemble classifiers , 2015, 2015 3rd International Conference on Signal Processing, Communication and Networking (ICSCN).

[23]  J. James.  Globalization, Information Technology and Development , 1999 .

[24]  Md. Rafiuzzaman Bhuiyan,et al.  Analysis Of Bangladeshi People's Emotion During Covid-19 In Social Media Using Deep Learning , 2020, 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT).

[25]  Shana Meganck,et al.  Ebola on Instagram and Twitter: How health organizations address the health crisis in their social media engagement , 2017 .

[26]  Sumayh S. Aljameel,et al.  A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia , 2020, International journal of environmental research and public health.

[27]  Patrick Paroubek,et al.  Twitter as a Corpus for Sentiment Analysis and Opinion Mining , 2010, LREC.

[28]  Vaishali Kalra,et al.  Importance of Text Data Preprocessing & Implementation in RapidMiner , 2018, ICITKM.

[29]  C. Papageorgiou,et al.  Mental Health Effects of COVID-19 Pandemia: A Review of Clinical and Psychological Traits , 2020, Psychiatry investigation.

[30]  Anju Bala,et al.  Analyzing Twitter sentiments through big data , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[31]  Weitong Chen,et al.  A survey of sentiment analysis in social media , 2018, Knowledge and Information Systems.

[32]  Reda Alhajj,et al.  Emotion and sentiment analysis from Twitter text , 2019, J. Comput. Sci..