Indo LEGO-ABSA: A Multitask Generative Aspect Based Sentiment Analysis for Indonesian Language

Aspect-based sentiment analysis is a method in natural language processing aimed at identifying and understanding sentiments related to specific aspects of an entity. Aspects are words or phrases that represent an aspect or attribute of a particular entity. Previous research has utilized generative pre-trained language models to perform aspect-based sentiment analysis. LEGO-ABSA is one framework that has successfully employed generative pre-trained language models in aspect-based sentiment analysis, particularly in English. LEGO-ABSA uses a multitask learning and prompting approach to enhance model performance. However, the application of this approach has not been done in the context of Bahasa Indonesia. Therefore, this research aims to implement the multitask learning and prompting approach in aspect-based sentiment analysis for Bahasa Indonesia using generative pre-trained language models. In this study, the Indo LEGO-ABSA model is developed, which is an aspect-based sentiment analysis model utilizing generative pre-trained language models and trained with multitask learning and prompting. Indo LEGO-ABSA is trained with a hotel domain dataset in the Indonesian language. The obtained results include an f1-score of 79.55% for the Aspect Sentiment Triplet Extraction task, 86.09% for Unified Aspect-based Sentiment Analysis, 79.85% for Aspect Opinion Pair Extraction, 87.45% for Aspect Term Extraction, and 88.09% for Opinion Term Extraction. Indo LEGO-ABSA adopts the LEGO-ABSA framework that employs the T5 model, specifically mT5, by applying multitask learning to train all tasks within aspect-based sentiment analysis.

Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies

This study conducts a thorough evaluation of text augmentation techniques across a variety of datasets and natural language processing (NLP) tasks to address the lack of reliable, generalized evidence for these methods. It examines the effectiveness of these techniques in augmenting training sets to improve performance in tasks such as topic classification, sentiment analysis, and offensive language detection. The research emphasizes not only the augmentation methods, but also the strategic order in which real and augmented instances are introduced during training. A major contribution is the development and evaluation of Modified Cyclical Curriculum Learning (MCCL) for augmented datasets, which represents a novel approach in the field. Results show that specific augmentation methods, especially when integrated with MCCL, significantly outperform traditional training approaches in NLP model performance. These results underscore the need for careful selection of augmentation techniques and sequencing strategies to optimize the balance between speed and quality improvement in various NLP tasks. The study concludes that the use of augmentation methods, especially in conjunction with MCCL, leads to improved results in various classification tasks, providing a foundation for future advances in text augmentation strategies in NLP.

Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

Mining and analysis of the big data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of tweets related to Ebola, E-Coli, Dengue, Human Papillomavirus, Middle East Respiratory Syndrome, Measles, Zika virus, H1N1, influenza like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinsons, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as catalysts for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both of these viruses. None of the prior works in this field analyzed tweets focusing on both COVID-19 and MPox simultaneously. To address this research gap, a total of 61,862 tweets that focused on MPox and COVID-19 simultaneously, posted between 7 May 2022 and 3 March 2023, were studied. The findings and contributions of this study are manifold. First, the results of sentiment analysis using the VADER approach show that nearly half the tweets had a negative sentiment. It was followed by tweets that had a positive sentiment and tweets that had a neutral sentiment, respectively. Second, this paper presents the top 50 hashtags used in these tweets. Third, it presents the top 100 most frequently used words in these tweets after performing tokenization, removal of stopwords, and word frequency analysis. Finally, a comprehensive comparative study that compares the contributions of this paper with 49 prior works in this field is presented to further uphold the relevance and novelty of this work.

Modelling Sentiment Analysis: LLMs and data augmentation techniques

This paper provides different approaches for a binary sentiment classification on a small training dataset. LLMs that provided state-of-the-art results in sentiment analysis and similar domains are being used, such as BERT, RoBERTa and XLNet.

Easy Data Augmentation in Sentiment Analysis of Cyberbullying

Instagram, a social media platform, has in the vicinity of 2 billion active users in 2023. The platform allows users to post photos and videos with one another. However, cyberbullying remains a significant problem for about 50% of young Indonesians. To address this issue, sentiment analysis for comment filtering uses a Support Vector Machine (SVM) and Easy Data Augmentation (EDA). EDA will augment the dataset, enabling robust prediction and analysis of cyberbullying by introducing more variation. Based on the tests, SVM combination with EDA results in a 2.52% increase in the k-Fold Cross Validation score. Our proposed approach shows an improved accuracy of 92.5%, 2.5% higher than that of the existing state-of-the-art method. To maintain the reproducibility and replicability of this research, the source code can be accessed at uns.id/eda_svm.

Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis

Artificial intelligence and machine learning have significantly bolstered the technological world. This paper explores the potential of transfer learning in natural language processing focusing mainly on sentiment analysis. The models trained on the big data can also be used where data are scarce. The claim is that, compared to training models from scratch, transfer learning, using pre-trained BERT models, can increase sentiment classification accuracy. The study adopts a sophisticated experimental design that uses the IMDb dataset of sentimentally labelled movie reviews. Pre-processing includes tokenization and encoding of text data, making it suitable for NLP models. The dataset is used on a BERT based model, measuring its performance using accuracy. The result comes out to be 100 per cent accurate. Although the complete accuracy could appear impressive, it might be the result of overfitting or a lack of generalization. Further analysis is required to ensure the model's ability to handle diverse and unseen data. The findings underscore the effectiveness of transfer learning in NLP, showcasing its potential to excel in sentiment analysis tasks. However, the research calls for a cautious interpretation of perfect accuracy and emphasizes the need for additional measures to validate the model's generalization.

SNNLP: Energy-Efficient Natural Language Processing Using Spiking Neural Networks

As spiking neural networks receive more attention, we look toward applications of this computing paradigm in fields other than computer vision and signal processing. One major field, underexplored in the neuromorphic setting, is Natural Language Processing (NLP), where most state-of-the-art solutions still heavily rely on resource-consuming and power-hungry traditional deep learning architectures. Therefore, it is compelling to design NLP models for neuromorphic architectures due to their low energy requirements, with the additional benefit of a more human-brain-like operating model for processing information. However, one of the biggest issues with bringing NLP to the neuromorphic setting is in properly encoding text into a spike train so that it can be seamlessly handled by both current and future SNN architectures. In this paper, we compare various methods of encoding text as spikes and assess each method's performance in an associated SNN on a downstream NLP task, namely, sentiment analysis. Furthermore, we go on to propose a new method of encoding text as spikes that outperforms a widely-used rate-coding technique, Poisson rate-coding, by around 13\% on our benchmark NLP tasks. Subsequently, we demonstrate the energy efficiency of SNNs implemented in hardware for the sentiment analysis task compared to traditional deep neural networks, observing an energy efficiency increase of more than 32x during inference and 60x during training while incurring the expected energy-performance tradeoff.

Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA), a nuanced task in text analysis, seeks to discern sentiment orientation linked to specific aspect terms in text. Traditional approaches often overlook or inadequately model the explicit syntactic structures of sentences, crucial for effective aspect term identification and sentiment determination. Addressing this gap, we introduce an innovative model: Syntactic Dependency Enhanced Multi-Task Interaction Architecture (SDEMTIA) for comprehensive ABSA. Our approach innovatively exploits syntactic knowledge (dependency relations and types) using a specialized Syntactic Dependency Embedded Interactive Network (SDEIN). We also incorporate a novel and efficient message-passing mechanism within a multi-task learning framework to bolster learning efficacy. Our extensive experiments on benchmark datasets showcase our model's superiority, significantly surpassing existing methods. Additionally, incorporating BERT as an auxiliary feature extractor further enhances our model's performance.

Probing the Information Encoded in Neural-based Acoustic Models of Automatic Speech Recognition Systems

Deep learning architectures have made significant progress in terms of performance in many research areas. The automatic speech recognition (ASR) field has thus benefited from these scientific and technological advances, particularly for acoustic modeling, now integrating deep neural network architectures. However, these performance gains have translated into increased complexity regarding the information learned and conveyed through these black-box architectures. Following many researches in neural networks interpretability, we propose in this article a protocol that aims to determine which and where information is located in an ASR acoustic model (AM). To do so, we propose to evaluate AM performance on a determined set of tasks using intermediate representations (here, at different layer levels). Regarding the performance variation and targeted tasks, we can emit hypothesis about which information is enhanced or perturbed at different architecture steps. Experiments are performed on both speaker verification, acoustic environment classification, gender classification, tempo-distortion detection systems and speech sentiment/emotion identification. Analysis showed that neural-based AMs hold heterogeneous information that seems surprisingly uncorrelated with phoneme recognition, such as emotion, sentiment or speaker identity. The low-level hidden layers globally appears useful for the structuring of information while the upper ones would tend to delete useless information for phoneme recognition.

Comparison of Topic Modelling Approaches in the Banking Context

Topic modelling is a prominent task for automatic topic extraction in many applications such as sentiment analysis and recommendation systems. The approach is vital for service industries to monitor their customer discussions. The use of traditional approaches such as Latent Dirichlet Allocation (LDA) for topic discovery has shown great performances, however, they are not consistent in their results as these approaches suffer from data sparseness and inability to model the word order in a document. Thus, this study presents the use of Kernel Principal Component Analysis (KernelPCA) and K-means Clustering in the BERTopic architecture. We have prepared a new dataset using tweets from customers of Nigerian banks and we use this to compare the topic modelling approaches. Our findings showed KernelPCA and K-means in the BERTopic architecture-produced coherent topics with a coherence score of 0.8463.