Data Mining Techniques to Categorize Single Paragraph-Formed Self-narrated Stories

Haque, Md. Mahmudul; Biswas, Niloy; Roy, Niloy Saha; Rafi, Amzad Hossain; Islam, Sajid-ul; Lubaba, Syeda Saiara; Islam, Rakibul; Rahman, Rashedur M.

doi:10.1007/978-981-15-8354-4_70

Cited by 2 publications

(3 citation statements)

References 6 publications

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“…DM is like a continuous bag of words from which interns create a dynamic sliding window and attempt to capture the relevant meaning of the words by sliding it through the sentence and the entire word corpus. It functions as a memory network that extracts the current context and changes the topic from one paragraph to the next [49]. Following tokenization, a single paragraph is created which comprises of the tokenized words as well as the appropriate tag.…”

Section: Doc2vector (Doc2vec)mentioning

confidence: 99%

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19

Kour

Gupta

2022

Neural Process Lett

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COVID-19 is a novel virus that presents challenges due to a lack of consistent and in-depth research. The news of the COVID-19 spreads across the globe, resulting in a flood of posts on social media sites. Apart from health, social, and economic disturbances brought by the COVID-19 pandemic, another important consequence involves public mental health crises which is of greater concern. Data related to COVID-19 is a valuable asset for researchers in understanding people's feelings related to the pandemic. It is thus important to extract the early information evolving public sentiments on social platforms during the outbreak of COVID-19. The objective of this study is to look at people's perceptions of the COVID-19 pandemic who interact with each other and share tweets on the Twitter platform. COVIDSenti, a large-scale benchmark dataset comprising 90,000 COVID-19 tweets collected from February to March 2020, during the initial phases of the outbreak served as the foundation for our experiments. A pre-trained bidirectional encoder representations from transformers (BERT) model is finetuned and embeddings generated are combined with two long short-term memory networks to propose the residual encoder transformation network model. The proposed model is used for multiclass text classification on a large dataset labeled as positive, negative, and neutral. The experimental outcomes validate that: (1) the proposed model is the best performing model, with 98% accuracy and 96% F1-score; (2) It also outperforms conventional machine learning algorithms and different variants of BERT, and (3) the approach achieves better results as compared to state-of-the-art on different benchmark datasets. KeywordsCOVID-19 • Transfer learning • Tweets • Sentiment analysis • LSTM • BERT Abbreviations BERT Bidirectional encoder representations from transformers LSTM Long short-term memory network RETN Residual encoder transformation network SARS-CoV-2 Severe acute respiratory syndrome -Coronavirus

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Section: Doc2vector (Doc2vec)mentioning

confidence: 99%

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19

Kour

Gupta

2022

Neural Process Lett

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“…Putri et al [8] classified QPQ and HWE on tweets with the hashtag #MeToo using the naïve Bayes algorithm with an accuracy of 88.5%. Haque et al [9] proved that k-nearest neighbour (KNN) bests other methods in sentiment analysis related to sexual harassment. However, imbalanced data are often present in classes of sexual harassment classification on specific datasets [10].…”

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confidence: 99%

“…In the classification process, the term frequency-inverse document frequency (TF-IDF) method is used as the document weighting method. Then, naïve Bayes and (KNN) methods are two legacy machine learning techniques that are compared to classify the harassment experiences [8] [9].…”

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Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE

Putrada

Wijaya²,

Oktaria³

2022

ijoict

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Delivery of justice with the help of artificial intelligence is a current research interest. Machine learning with natural language processing (NLP) can classify the types of sexual harassment experiences into quid pro quo (QPQ) and hostile work environments (HWE). However, imbalanced data are often present in classes of sexual harassment classification on specific datasets. Data imbalance can cause a decrease in the classifier's performance because it usually tends to choose the majority class. This study proposes the implementation and performance evaluation of the synthetic minority over-sampling technique (SMOTE) to improve the QPQ and HWE harassment classifications in the sexual harassment experience dataset. The term frequency-inverse document frequency (TF-IDF) method applies document weighting in the classification process. Then, we compare naïve Bayes with K-Nearest Neighbor (KNN) in classifying sexual harassment experiences. The comparison shows that the performance of the naïve Bayes classifier is superior to the KNN classifier in classifying QPQ and HWE, with AUC values of 0.95 versus 0.92, respectively. The evaluation results show that by applying the SMOTE method to the naïve Bayes classifier, the precision of the minority class can increase from 74% to 90%.

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Data Mining Techniques to Categorize Single Paragraph-Formed Self-narrated Stories

Cited by 2 publications

References 6 publications

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19

AI Assisted Attention Mechanism for Hybrid Neural Model to Assess Online Attitudes About COVID-19

Overcoming Data Imbalance Problems in Sexual Harassment Classification with SMOTE

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