Aspect-based Sentiment Analysis on Product Reviews using Enhanced Bidirectional LSTM

According to the World Health Organization (WHO), 17.9 million people die yearly from cardiovascular Diseases (CVDs), including heart attacks. Cardiovascular diseases, including heart attack, kill 32% of people globally. Current approaches struggle with electrocardiogram (ECG) signal variability, causing diagnosing errors. The adoption of automated and accurate models for heart disease detection is lacking since conventional methods rely on human analysis, which is time-consuming and error-prone. This work covers the crucial topic of heart disease diagnosis, especially ECG data analysis for cardiovascular disease detection. The integration of the Deep-Convolutional Neural Network (Deep-CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) model with an Attention Mechanism enhances the accuracy and reliability of heart disease categorisation. The Deep-CNN component efficiently extracts features from capture spatial linkages, while the Bi-LSTM layers handle temporal dependencies to identify patient health patterns over time. The model is evaluated on 303 patient records with 14 clinical characteristics from the University of California, Irvine (UCI) Cleveland Heart Disease dataset. The suggested technique has 97.23% accuracy, 97.72% recall, precision, and 96.90% F1 score. These findings show that the proposed architecture improves diagnostic performance more than boosting ensemble approaches and hybrid models.

Sentiment analysis or opinion mining used to capture the community's attitude who have experienced the specific service/product. Sentiment analysis usually concentrates to classify the opinion of whole document or sentence. However, in most comments, users often express their opinions on different aspects of the mentioned entity rather than express general sentiments on entire document. In this case, using aspect-based sentiment analysis (ABSA) is a solution. ABSA emphases on extracting and synthesizing sentiments on particular aspects of entities in opinion text. The previous studies have difficulty working with aspect extraction and sentiment polarity classification in multiple domains of review. We offer an innovative deep learning approach with the integrated construction of bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) for multidomain ABSA in this article. Our system finished the following tasks: domain classification, aspect extraction and opinion determination of aspect in the document. Besides applying GloVe word embedding for input sentences from mixed Laptop_Restaurant domain of the SemEval 2016 dataset, we also use the additional layer of POS to pick out the word morphological attributes before feeding to the CNN_BiLSTM architecture to enhance the flexibility and precision of our suggested model. Through experiment, we found that our proposed model has performed the above mentioned tasks of domain classification, aspect and sentiment extraction concurrently on a mixed domain dataset and achieved the positive results compared to previous models that were performed only on separated domain dataset.

Sharia stocks are one of the investment instruments in the Islamic capital market. In the capital market, it is known that stock prices are very volatile. This makes investors need to carry out a strategy for making the right decision in investing, one of which can be done by predicting stock prices. In this study, predictions were made using historical data on the closing price of Islamic shares of PT. Telkom Indonesia Tbk with the Bidirectional Long Short Term Memory (BiLSTM) method. In building the best prediction model, it is necessary to choose the right parameters and one way to do this is to use the grid search algorithm. Based on the results of the test analysis, it was found that the smallest Mean Absolute Percentage Error (MAPE) value was found in the BiLSTM model in the distribution of data with a percentage of 90% training data and 10% testing data and parameter values obtained based on parameter tuning using grid search, including the number of neurons 25, 100 epochs, 4 batches, and 0.2 dropouts. The MAPE obtained in this study was 10.83% and based on the scale on the MAPE value criteria, this shows that the resulting prediction model is accurate. As for the test results from the comparisons made on the BiLSTM and LSTM models using grid search as a tuning parameter and models without using a grid search or it can be called a trial and error approach as a tuning parameter, it is found that the model with better predictive performance is found in BiLSTM using a grid search. compared to other models.

People post their emotional expressions about specific events through social media platforms, such as praise and complaints about city bus travel services. In this paper, we propose a social media-based method to analyze bus service satisfaction. Firstly, we collect microblogs related to bus service from Sina Weibo, and pre-process the collected microblogs. Then the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model is combined with Bi-directional Long Short-Term Memory (BiLSTM) model to form BERT- BiLSTM model, using the pre-processed data to train the BERT-BiLSTM model to realize the classification of text data into positive and negative sentiments, and finally using the method of this paper to analyze and verify the satisfaction of Guangzhou bus services system in 2010. Comparing the method of this paper with traditional machine learning and deep learning methods, the experiments show the effectiveness of the BERT-BiLSTM model. KeywordsSocial mediaBus serviceSentiment analysisBERT-BiLSTMBinary classification

For many natural language processing applications, part of speech (POS) tagging remains as a preliminary task. Marathi, is observed as a popular language in India but it only has limited tools and corpus for NLP applications. An accurate POS tagger is essential for many NLP tasks like sentiment analysis, named entity recognition, dependency parsing, etc. This research work proposes a deep learning model and bidirectional long short-term memory (Bi-LSTM) model to perform POS tagging for Marathi text. We achieved an accuracy of 85% for the deep learning model and 97% for the Bi-LSTM model. Our contribution here is based on three folds - building a deep learning model, building the Bi-LSTM model, comparison with machine learning techniques for the same dataset.

In this study, the Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to optimize the unbalanced allocation mechanism in the electricity market, and an empirical analysis is made based on the Nord Pool dataset. It is found that the Bi-LSTM model is significantly better than the traditional method in reducing the total deviation power and unbalanced cost. Specifically, the proposed model reduces the total deviation power by 16.5% and the unbalanced cost by 12.3%. The incentive compatibility index shows that the incentive effect of the proposed model reaches 27.0%. It is higher than that of the F1 mechanism (23.5%) and the F2 mechanism (25.5%). In terms of cost allocation efficiency, the mean square error of the proposed model is 0.018. This shows higher cost allocation accuracy compared with 0.028 in the F1 mechanism and 0.022 in the F2 mechanism. These results show that the proposed model has obvious advantages in improving market operation efficiency, encouraging market participants to reduce deviation electricity, and realizing fair cost allocation. The research conclusion provides a new technical path for the design of the allocation mechanism in the power market and provides strong support for future market management and optimization.

The proliferation of misinformation, as insidious and pervasive as water, presents an unprecedented challenge to public discourse and comprehension. Often propagated to further specific ideologies or political objectives, misinformation not only misleads the populace but also fuels online advertising revenue generation. As such, the urgent need to pinpoint and eliminate misinformation from digital platforms has never been more critical. In response to this dilemma, this paper proposes a solution built on the backbone of massive data generation in today’s digital landscape. By leveraging advanced technologies, such as AI-driven systems with deep learning models and natural language processing capabilities, we can monitor and analyze an extensive scope of social media data. This, in turn, facilitates the identification of misinformation across multiple platforms and alerts users to potential propaganda. Central to our study is the development of misinformation classifiers based on a deep bi-directional long short-term memory (Bi-LSTM) model. This model is further enhanced by employing a genetic algorithm (GA), which automates the search for an optimal neural architecture, thereby significantly impacting the training behavior of the deep learning algorithm and the performance of the model being trained. To validate our approach, we compared the efficacy of our proposed model with nine traditional machine learning algorithms and a deep learning model rooted in long short-term memory (LSTM). The results affirmed the superiority of our GA-tuned Bi-LSTM model, which outperformed all other models in detecting misinformation with remarkable accuracy. Our intention with this paper is not to present our model as a comprehensive solution to misinformation but rather as a technological tool that can aid in the process, supplementing and bolstering the existing methodologies in the field of misinformation detection.

The rapid growth of AI-generated content from ChatGPT, Gemini, and many others poses significant challenges in digital media. There are increasing instances of text generated by machine learning models being indistinguishable from human-written text. This calls for an effective way of detecting and distinguishing text generated by AI from human text. Using a Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, a Bidirectional Long Short-Term Memory (Bi-LSTM) model for classifying AI-generated text in a digital media context was developed. A carefully curated dataset of human and AI-generated text samples was used. The Bi-LSTM model without an embedding layer was implemented, optimizing the model to capture complex linguistic patterns apparent in each text type. An experimental setup was used to evaluate the effectiveness of the model. It was noted that the model achieved a remarkable test accuracy of 99.79%, with a loss of 0.009. To facilitate practical implementation, we developed a web application using Next.js with our model served from a Flask server that enables real-time AI text detection. Our results highlight the model’s ability to accurately identify AI-generated text, providing valuable insights into deploying such models to verify content on media platforms. This study highlights the potential of neural network-based classifiers to address the pressing need for automated AI text detection in an increasingly AI-influenced digital ecosystem.

Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts

Problem definition: The intelligent transportation system (ITS) plays a vital role in the construction of smart cities. For the past few years, traffic flow prediction has been a hot study topic in the field of transportation. Facing the rapid increase in the amount of traffic information, finding out how to use dynamic traffic information to accurately predict its flow has become a challenge. Methodology: Thus, to figure out this issue, this study put forward a multistep prediction model based on a convolutional neural network and bidirectional long short-term memory (BILSTM) model. The spatial characteristics of traffic data were considered as input of the BILSTM model to extract the time series characteristics of the traffic. Results: The experimental results validated that the BILSTM model improved the prediction accuracy in comparison to the support vector regression and gated recurring unit models. Furthermore, the proposed model was comparatively analyzed in terms of mean absolute error, mean absolute percentage error, and root mean square error, which were reduced by 30.4%, 32.2%, and 39.6%, respectively. Managerial implications: Our study provides useful insights into predicting the short-term traffic flow on highways and will improve the management of traffic flow optimization.

This study examines the efficacy of three distinct machine learning models for sentiment analysis: Bidirectional Long Short-Term Memory (Bi-LSTM), Long Short-Term Memory (LSTM), and Logistic Regression. One of the most important tasks in natural language processing is sentiment analysis, which entails categorizing reviews as positive or negative. Fifty thousand sentiment-labeled movie reviews make up the dataset. To determine the most precise and effective technique for sentiment categorization, we put these models into practice and evaluated their respective performances. The highest accuracy of 89.42% is achieved by Logistic Regression, with precision of 88.35%, recall of 91.01%, and F1-score of 89.66%. The LSTM model, well-known for capturing temporal dependencies in text, obtained an accuracy of 86.23%, precision of 89.60%, recall of 82.22%, and an F1-score of 85.75%. The Bi-LSTM model, which processes input sequences in both forward and backward directions to better capture context, demonstrated accuracy of 87.65%, precision of 88.67%, recall of 86.54%, and an F1-score of 87.60%. The results show that although Logistic Regression performs better in accuracy, the Bi-LSTM model offers a substantial trade-off between precision and recall, making it a viable option for sentiment analysis applications. Despite its minor accuracy lag, the LSTM model provides important insights into the sequential relationships of the text data.

In cybersecurity, the rise of fileless malware poses a significant challenge to endpoint security. Traditional detection methods often fail against these sophisticated attacks, necessitating advanced techniques like deep learning models. This study highlights the limitations of Bi-Directional Long Short-Term Memory (BLSTM) models in dynamic malware analysis and proposes enhancements through Convolutional Long Short-Term Memory (ConvLSTM) architecture. BLSTM models process input sequences in forward and backward directions, combining the results into one output. While this dual-layer approach improves analysis, it is time-consuming, potentially increasing the risk of fileless malware attacks. A key limitation of BLSTM is the lack of parameter sharing between forward and backward directions. This reduces its ability to capture spatial and temporal features simultaneously, hindering effectiveness in detecting fileless malware. To address this, the ConvLSTM model consolidates feature extraction within a single LSTM cell layer. ConvLSTM breaks down samples into subsequence and uses timesteps for additional feature extraction, enabling spatial-temporal data analysis and improving malware prediction accuracy. The model was tested using a dynamic malware dataset. Unlike traditional LSTM, ConvLSTM integrates convolutional layers, allowing parameter sharing across both spatial and temporal dimensions. This reduces computational complexity and improves model performance in handling multidimensional data. The research re-simulated prior work with BLSTM using the same malware dataset. The Spyder app ran the event simulator and the ConvLSTM model's results replaced BLSTM's using identical parameters. Time, accuracy and loss were the main performance metrics. ConvLSTM outperformed BLSTM, achieving 98% detection accuracy compared to BLSTM's 90%. It also significantly reduced processing time, averaging 10 seconds, while BLSTM took 22 seconds. ConvLSTM experienced lower losses, averaging 10% per epoch versus BLSTM's 20%. In conclusion, ConvLSTM offers superior performance over BLSTM in fileless malware detection. Its enhanced computational efficiency and ability to quickly mitigate threats make it a robust solution for fortifying endpoint security against evolving cyber threats. ConvLSTM holds potential in strengthening defence mechanisms against sophisticated malware attacks, providing a proactive approach to safeguarding networks and data.

With the widespread operation of high-speed trains, traction motors are susceptible to failures under high-temperature conditions and complex working scenarios. This leads to abnormal temperature increases, which pose significant threats to train safety. Therefore, researching the temperature prediction of traction motors is of great significance for ensuring the safe operation of trains. This paper proposes a temperature prediction method based on an improved bidirectional long short-term memory (Bi-LSTM) network. Initially, convolutional neural networks (CNN) are employed to extract temporal structures from the data. Simultaneously, the squeeze-and-excitation (SE) attention mechanism is integrated with the key-query-value (KQV) mechanism. Finally, Bi-LSTM is utilized for temperature prediction. The proposed SE-BiLSTM-CNN model exhibits significantly enhanced prediction performance compared with recurrent neural networks (RNN), gated recurrent units (GRU), LSTM, and Bi-LSTM models, with improvements of 35.51, 56.07, and 33.75% in the evaluation metrics of MAE, MSE, and RMSE, respectively.

In this paper, a combination model based on complementary ensemble empirical mode decomposition (CEEMD) is proposed. First, CEEMD is applied to decompose original network traffic to generate high‐frequency component, low‐frequency component, and residual component. Then, the high‐frequency components are modeled and predicted using bi‐directional long short‐term memory (BiLSTM). The low‐frequency components and the residual component are modeled and predicted using autoregressive integrated moving average (ARIMA). Meanwhile, considering that the BiLSTM model is influenced by the hyperparameters, an Improved Bald Eagle Search (IBES) algorithm is proposed and applied to optimize three hyperparameters of BiLSTM, avoiding the blindness and subjectivity of manual selection of parameters. Finally, the prediction values of BiLSTM and ARIMA model are summed to obtain the final predicted value of network traffic. The comparisons with other models proved that the proposed network traffic prediction model is closer to the real data, with the optimal performance indicators, which is very suitable for high precision occasions.

This study addresses the accuracy challenges in e-commerce sentiment classification and thus provides valuable insight for businesses to enrich strategies toward the interpretation of customer feedback and improvement of product development. This article elaborately contrasts long short-term memory (LSTM)-based models with traditional machine learning models, like support vector machines (SVM), random forest, and Naive Bayes classifiers. The authors have used a large dataset of customer reviews from famous e-commerce websites, pre-processed for noise reduction and standardization of the input. Our LSTM models were implemented using state-of-the-art deep learning frameworks, with special consideration while performing hyperparameter tuning. The results showed that the bidirectional LSTM model had the best performance of 92.1% in accuracy, 91.8% in precision, 92.0% in recall, and an F1-score of 91.9%. In comparison, traditional machine learning approaches gave accuracies: 86.5% for SVM, 85.1% for random forest, and 82% for Naive Bayes. Hyperparameter tuning returned the best configurations for the LSTM models: 128 LSTM units, a dropout of 0.3, with learning rates 0.001 for LSTM and 0.0001 for bidirectional LSTM. These optimizations contributed hugely to the performance improvements observed in these models. Error analysis gave insights into the challenges that the models faced. Sarcasm and irony accounted for 22% of the classification errors, while mixed sentiment accounted for 18%, and implicit accounted for 15%. To sum up, this research has shown the efficiency of LSTM models on e-commerce user review sentiment analysis, especially bidirectional LSTM. These models show a much better result in sentiment classification compared to traditional machine learning techniques, which proves them to have huge potential for improving the accuracy of the task in real-world applications.