Develop a Neural Model to Score Bigram of Words Using Bag-of-Words Model for Sentiment Analysis

Balamurali, A. R.; Ananthanarayanan, Balamurali

doi:10.4018/978-1-7998-1159-6.ch008

Cited by 5 publications

(4 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep neural networks have been successfully employed for different types of machine-learning tasks, such as NLP-based methods utilizing sentiment aspects for deep classification [21]- [26]. Deep neural networks are able to model high-level abstractions and to decrease the dimensions by utilizing multiple processing layers based on complex structures or to be combined with non-linear transformations.…”

Section: Deep Learning and Covid-19-sentiment Classificationmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

Wang

Orji

et al. 2020

IEEE J. Biomed. Health Inform.

303

166

View full text Add to dashboard Cite

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15%-a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

show abstract

Section: Deep Learning and Covid-19-sentiment Classificationmentioning

confidence: 99%

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Jelodar

Wang

Orji

et al. 2020

IEEE J. Biomed. Health Inform.

303

166

View full text Add to dashboard Cite

show abstract

“…Bag of Words (BoW) [23] is a common feature extraction method that involves a vocabulary of known words and a measure of the presence of known words. The BoW is only concerned with known words in a document.…”

Section: A Feature Extractionmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

Tao

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

show abstract

“…After tweaking, the number of topics and words was set to 5 and 10, respectively. The words in each topic were tokenised with the help of Gensim’s simple_preprocess() and grouped into bigrams and trigrams[27], which are 2 and 3 frequently occurring words, respectively, using a Phrases model. This is followed by mapping each word with Gensim’s id2word and further interpreting keywords as an interactive web-based visualisation with pyLDAvis[28].…”

Section: Experiments Detailsmentioning

confidence: 99%

redBERT: A Topic Discovery and Deep Sentiment Classification Model on COVID-19 Online Discussions Using BERT NLP Model

Pandey

2021

Preprint

View full text Add to dashboard Cite

A Natural Language Processing (NLP) method was used to uncover various issues and sentiments surrounding COVID-19 from social media and get a deeper understanding of fluctuating public opinion in situations of wide-scale panic to guide improved decision making with the help of a sentiment analyser created for the automated extraction of COVID-19 related discussions based on topic modelling. Moreover, the BERT model was used for the sentiment classification of COVID-19 Reddit comments. These findings shed light on the importance of studying trends and using computational techniques to assess human psyche in times of distress.

show abstract

Develop a Neural Model to Score Bigram of Words Using Bag-of-Words Model for Sentiment Analysis

Cited by 5 publications

References 4 publications

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

redBERT: A Topic Discovery and Deep Sentiment Classification Model on COVID-19 Online Discussions Using BERT NLP Model

Contact Info

Product

Resources

About