Sentiment Classifier and Analysis for Epidemic Prediction

Adhikari, Nimai Chand Das; Kurva, Vamshi Kumar; Suhas, S; Kushwaha, Jitendra Kumar; Nayak, Ashish Kumar; Nayak, Sankalp Kumar; Shaj, Vaisakh

doi:10.5121/csit.2018.81004

Cited by 11 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The word clouds represent the image of text, where the size of the phrase is proportionate to the prevalence of occurrence. Therefore, the more prominent phrase expresses the most frequent words posted in tweets [51]. At a glance, the word cloud of COVID-19 vaccine shows more common words discussed by Sentiment_Type object people such as "Covid-vaccine", "Russia", "First Covid", "people", and "coronavirus", "trial", and "health ministry".…”

Section: Data Visualizationmentioning

confidence: 99%

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Tiwari

Nagpal

2022

New Gener. Comput.

View full text Add to dashboard Cite

Social media materialized as an influential platform that allows people to share their views on global and local issues. Sentiment analysis can handle these massive amounts of unstructured reviews and convert them into meaningful opinions. Undoubtedly, COVID-19 originated as the enormous challenge across the world that physically and financially bruted humankind. Meanwhile, farmers' protests shook up the world against three pieces of legislation passed by the Indian government. Hence, an artificial intelligence-based sentiment model is needed for suggesting the right direction toward outbreaks. Although Deep Neural Network (DNN) gained popularity in sentiment analysis applications, these still have a limitation of sequential training, high-dimension feature space, and equal feature importance distribution. In addition, inaccurate polarity scoring and utility-based topic modeling are other challenging aspects of sentiment analysis. It motivates us to propose a Knowledge-Enriched Attention-based Hybrid Transformer (KEAHT) model by enriching the explicit knowledge of Latent Dirichlet Allocation (LDA) topic modeling and lexicalized domain ontology. A pre-trained Bidirectional Encoder Representation from Transformer (BERT) is employed to train within a minimum training corpus. It provides the facility of attention mechanism and can solve complex text problems accurately. A comparative study with existing baselines and recent hybrid models affirms the credibility of the proposed KEAHT in the field of Natural Language Processing (NLP). This model emphasizes artificial intelligence's role in handling the situation of the global pandemic and democratic dispute in a country. Furthermore, two benchmark datasets, namely “COVID-19-Vaccine-Labelled-Tweets" and "Indian-Farmer-Protest-Labelled-Tweets”, are also constructed to accommodate future researchers for outlining the essential facts associated with the outbreaks.

show abstract

Section: Data Visualizationmentioning

confidence: 99%

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Tiwari

Nagpal

2022

New Gener. Comput.

View full text Add to dashboard Cite

show abstract

“…Sentiment analysis has been applied to predict the evolution of the stock market [7], or to predict commercial success based on sentiments from hotel reviews [8], movie reviews [9], or restaurant reviews [9], or to predict risk phenomena, e.g. crime [10] or epidemic outbreaks [11].…”

Section: Sentiment Analysismentioning

confidence: 99%

Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice

2020

2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

View full text Add to dashboard Cite

This paper analyzes how candidate choice prediction improves by different psychological predictors. To investigate this question, it collected an original survey dataset featuring the popular TV series "Game of Thrones". The respondents answered which character they anticipated to win in the final episode of the series, and explained their choice of the final candidate in free text from which sentiments were extracted. These sentiments were compared to feature sets derived from candidate likeability and candidate personality ratings. In our benchmarking of 10-fold cross-validation in 100 repetitions, all feature sets except the likeability ratings yielded a 10-11% improvement in accuracy on the holdout set over the base model. Treating the class imbalance with synthetic minority oversampling (SMOTE) increased holdout set performance by 20-34% but surprisingly not testing set performance. Taken together, our study provides a quantified estimation of the additional predictive value of psychological predictors. Likeability ratings were clearly outperformed by the feature sets based on personality, emotional valence, and basic emotions.

show abstract

“…Ishtiaq Ahmed et al [13] have suggested a hybrid SMS classification system for detecting harm or spam, through the use of the Apriori algorithm and Naïve Bayes classifier. Even though this approach is fully logic-based, the performance of the method depends on the database's statistical character.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

Hussein¹,

Kareem²

2019

Journal of Southwest Jiaotong University

View full text Add to dashboard Cite

Sentiment analysis is examined within natural language processing. It is instrumental in finding the sentiment (feeling) or opinion (idea) hidden within a text. This research focuses on finding sentiments in a “text image” and then classifying them whether they are desirable or not. These phrases and words refer to the perspectives of people about anything they think about it, such as services, products, governments, and social media events. In this study, the optical character recognition (OCR) algorithm was used, which is considered as a classification procedure of visual patterns that appear in the form of a digital image. Moreover, the Naïve Bayes machine learning algorithm was employed to classify these texts. These two algorithms form a hybrid system that supports our needs, especially in this day of technological advances and frequent use of websites and sharing of text images through the internet. Finally, the new vision in this work involves dealing with Arabic language texts that are transformed into images, which are extracted from a URL address and then classified into desirable and undesirable content.

show abstract

Sentiment Classifier and Analysis for Epidemic Prediction

Cited by 11 publications

References 0 publications

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis

Who Wins the Game of Thrones? How Sentiments Improve the Prediction of Candidate Choice

A Proposed Arabic Text and Text Image Classification Technique Using a URL Address

Contact Info

Product

Resources

About