Because of the current COVID-19 pandemic with its increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected the developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets/comments posted online after their proper analysis and sentiment classification. Nevertheless, owing to the limited number of tokens/words in each tweet, it is always crucial to capture multiple information associated with them for their better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag of words method, whereas the semantic information is generated from the combination of the fastTextbased (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel convolutional neural network (MCNN), which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral and negative) on NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in C. Sitaula Department of Electrical and Computer Systems Engineering Monash University Wellington Rd, Clayton VIC 3800, Australia E-mail: Chiranjibi.Sitaula@monash.edu TB. Shahi Central Department of Computer Science and Information Technology Tribhuvan University TU Rd, Kirtipur 44618, Kathmandu, Nepal E-mail: tejshahi@cdcsit.edu.np Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification.

pdf/multi-channel-cnn-to-classify-nepali-covid-19-related-tweets-1yog5j64.pdf

Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features

Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results. 

/pdf/game-theory-and-mcdm-based-unsupervised-sentiment-analysis-3dzjffrw.pdf

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Imbalanced data classification presents a challenge in machine learning, inducing biased model learning. Moreover, data dimensionality poses another challenge as it highly impacts classifier performance. This paper proposes a new deep-learning method that combines feature selection with oversampling to address these challenges. The proposed approach, GA-SMOTE-DCNN, integrates a genetic algorithm (GA) for feature selection, SMOTE for oversampling, and a deep 1D-convolutional neural network (DCNN) for classification. This study reveals that pre-splitting the data into training and testing sets before applying SMOTE results in higher accuracy, showing an improvement in accuracy ranging between 1.94% and 3.98% compared to post-SMOTE splitting for each dataset. This method achieved accuracy rates of 86.81% for the Balance Scale dataset, 86.15% for the Oil Spill dataset, 89.21% for the Yeast dataset, 91.32% for the Mammography dataset, 88.23% for the Australian credit dataset, and 89.53% for the German Credit dataset when compared with benchmark methods, underscoring its significance in tackling high-dimensional and imbalanced data classification problems. This method demonstrates scalability in effectively addressing challenges associated with high-dimensional and imbalanced data classification across various domains. 

Deep Convolutional Neural Networks with Genetic Algorithm-Based Synthetic Minority Over-Sampling Technique for Improved Imbalanced Data Classification

EIAASG: Emotional Intensive Adaptive Aspect-Specific GCN for sentiment classification

Effective inter-aspect words modeling for aspect-based sentiment analysis

Experiencer-Driven and Knowledge-Aware Graph Model for emotion–cause pair extraction

Aspect-based sentiment analysis with multi-granularity information mining and sentiment hint

With the increasing complexity of graph queries, query cost estimation has become a key challenge in graph databases. Accurate estimation results are critical for database administrators or database management systems to perform query processing or optimization tasks. An efficient and accurate estimation model can improve the estimation quality and make the produced results credible. Although learning-based methods have been applied in query cost estimation, most of them are directed at relational queries and cannot be directly used for graph queries. Furthermore, most estimation approaches focus on the correlations between predicates or columns. The dependencies between query schema and query filter conditions and the correlation between query schema are ignored. In this study, we construct a novel deep learning model composed of reasoning and retrieval processes that can accurately capture the potential logical relationships in graph queries. This solves the above problems to some extent. In addition, we propose a query estimation framework that divides the estimation task into query workload generation, training data collection, feature extraction and encoding, and estimation model construction. The results of the experiment on real-world datasets show that our estimation model can improve the estimation quality and outperforms other compared deep learning models in terms of estimation accuracy. 

Query cost estimation in graph databases via emphasizing query dependencies by using a neural reasoning network

Multi-Chain of Thought Prompt Learning for Aspect-Based Sentiment Analysis

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