Deep Learning Models to Detect Multi-word Expressions

BackgroundIt is essential for radiologists to communicate actionable findings to the referring clinicians reliably. Natural language processing (NLP) has been shown to help identify free-text radiology reports including actionable findings. However, the application of recent deep learning techniques to radiology reports, which can improve the detection performance, has not been thoroughly examined. Moreover, free-text that clinicians input in the ordering form (order information) has seldom been used to identify actionable reports. This study aims to evaluate the benefits of two new approaches: (1) bidirectional encoder representations from transformers (BERT), a recent deep learning architecture in NLP, and (2) using order information in addition to radiology reports.MethodsWe performed a binary classification to distinguish actionable reports (i.e., radiology reports tagged as actionable in actual radiological practice) from non-actionable ones (those without an actionable tag). 90,923 Japanese radiology reports in our hospital were used, of which 788 (0.87%) were actionable. We evaluated four methods, statistical machine learning with logistic regression (LR) and with gradient boosting decision tree (GBDT), and deep learning with a bidirectional long short-term memory (LSTM) model and a publicly available Japanese BERT model. Each method was used with two different inputs, radiology reports alone and pairs of order information and radiology reports. Thus, eight experiments were conducted to examine the performance.ResultsWithout order information, BERT achieved the highest area under the precision-recall curve (AUPRC) of 0.5138, which showed a statistically significant improvement over LR, GBDT, and LSTM, and the highest area under the receiver operating characteristic curve (AUROC) of 0.9516. Simply coupling the order information with the radiology reports slightly increased the AUPRC of BERT but did not lead to a statistically significant improvement. This may be due to the complexity of clinical decisions made by radiologists.ConclusionsBERT was assumed to be useful to detect actionable reports. More sophisticated methods are required to use order information effectively.

Suicide remains a global public health crisis, with millions expressing emotional distress and suicidal ideation on social media platforms, often without receiving timely intervention. This study explores the potential of Natural Language Processing (NLP) to detect suicide-related content by leveraging deep learning models trained on real-time user-generated text. Harnessing the power of language, the project aims to develop and benchmark cutting-edge NLP architectures capable of identifying suicidal ideation with high accuracy, precision, and recall. Unlike traditional clinical assessments, social media provides raw, unfiltered insight into individuals' mental states, offering a critical opportunity for early detection and preventive action. The research evaluates multiple deep learning models including Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM) networks, and hybrid CNN-LSTM architectures using labeled datasets from platforms such as Twitter and Reddit. Preprocessing techniques like tokenization, lemmatization, and noise reduction are applied to enhance semantic understanding. Furthermore, the study incorporates sentiment analysis, keyword extraction, and contextual embeddings to capture nuanced expressions of distress and intent. Performance is benchmarked across diverse metrics, including F1 score, Area Under the Curve (AUC), and Matthews Correlation Coefficient (MCC), with interpretability frameworks like LIME and SHAP used to ensure transparency in model predictions. The results demonstrate that contextual deep learning models significantly outperform traditional machine learning methods in detecting suicidal language patterns, even when disguised in metaphor, sarcasm, or indirect references. The study concludes by proposing an integrated framework for deploying these models in collaboration with mental health organizations, platform moderators, and emergency response services. Ethical considerations including user privacy, data anonymization, and false positive mitigation are emphasized to ensure responsible deployment. By bridging the gap between technology and mental health, this research underscores the transformative role of NLP in suicide prevention and advocates for AI-powered systems as a vital tool in safeguarding vulnerable populations in the digital age.

With the continuous evolution of natural language processing (NLP), conversational agents have become pivotal in enhancing user engagement and satisfaction in the e-commerce domain. This paper presents a comprehensive study and implementation of intent recognition and slot filling techniques tailored for an e-commerce chatbot. Leveraging a dataset of 8,000 samples, we employed a Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) networks for intent recognition, achieving an accuracy of 87%. For slot filling, we utilized the Bidirectional Encoder Representations from Transformers (BERT) model, also attaining an accuracy of 87%. The chatbot seamlessly integrates with an e-commerce database, using OpenAI's GPT-3 to generate natural language responses from query results. Our system demonstrates significant advancements in processing user queries, generating precise database queries, and providing coherent and relevant responses, thereby enhancing the overall user experience. This work aims to equip researchers and practitioners with insights into the methodologies and challenges in developing sophisticated e-commerce chatbots, fostering further innovation in this field. Key Words: Intent Recognition, Slot Filling, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), GPT-3, Natural Language Understanding (NLU), Chatbot

The price of oil is highly complex to predict as it is impacted by global demand and supply, geopolitical events, and market sentiment. The accuracy of such predictions, however, has far-reaching implications for supply chain performance, portfolio management, and expected stock market returns. This paper contributes to the oil price prediction literature by evaluating the predictive impact of the US President's communication on Twitter, while benchmarking various Natural Language Processing (NLP) techniques, including Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Doc2Vec, Global Vectors for Word Representation (GloVe), and Bidirectional Encoder Representations from Transformers (BERT). These techniques are combined with a deep neural network Long Short-Term Memory (LSTM) architecture using a five-day lag for both the oil price and the textual Twitter data. The data was collected during the term of US President Donald Trump, resulting in 1449 days of crude oil price prediction and a total of 16,457 tweets. The study is validated for Brent and West Texas Intermediate blends, using the daily price of a barrel of crude oil as the target variable. The results confirm that including the US President's tweets significantly increases the predictive power of oil price prediction models, and that an LSTM architecture with BERT as NLP technique has the best performance.

This study aims to enhance property market valuation in Malaysia by integrating AI-driven sentiment analysis with traditional econometric models and advanced machine learning (ML) techniques. The proposed framework combines ARIMA and GARCH models with deep learning algorithms such as Long-Short Terem Memory (LSTM) and Artificial Neural Network (ANN). Sentiment analysis leverages Natural Language Processing (NLP) through Transformer-based models, particularly Bidirectional Encoder Representation from Transformers (BERT), to extract market sentiment from property-related news, social media discussions, and corporate disclosures. The results demonstrate that this approach significantly improves valuation accuracy by capturing historical trends alongside real-time sentiment shifts. By merging sentiment analysis with property market data, the study offers a more comprehensive understanding of market dynamics, enabling more accurate predictions of price fluctuations and market movements. The research’s novelty lies in the integration of qualitative sentiment insights with quantitative property indicators, facilitating the timely detection of market changes and better insights into investor behaviour. Additionally, the model exhibits enhanced fairness and adaptability, ensuring consistent performance across Malaysia’s multilingual and diverse real estate market. The findings provide valuable implications for property investors, developers, and policymakers, as the enhanced models support better investment strategies, effective risk management, and informed decision-making in Malaysia’s evolving property landscape.

As social media and online social networking sites have emerged as essential channels for everyday social interactions, researchers are increasingly turning to conversational social media as a valuable alternative source of insights into public sentiment and behavior during times of crisis. In a similar vein, our study investigates the utility of using conversational social media data for analyzing public sentiment related to the COVID-19 pandemic. Our research question is, to what extent can machine learning models based on conversational social media data help gauge public sentiment during a pandemic? To evaluate the performance of various machine learning algorithms in sentiment analysis tasks, we used a Twitter dataset and compared their effectiveness using eight different models (i.e., Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Extreme Gradient Boosting (XGBoost), Logistic Regression, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT)). The results show that among the eight models, the top three models are BERT, LSTM, and SVM, achieving overall accuracies of 87.44%, 81.94%, and 80.02%, respectively. Employing sentiment analysis models on pandemic-related social media posts allows us to gain insights into of human emotions and sentiments during public health crises like a pandemic.

Traditional Chinese medicine diagnostic prediction model for holistic syndrome differentiation based on deep learning

Abstract—This paper presents a hybrid sentiment analysis system that integrates lexicon-based, traditional machine learning, and deep learning techniques to classify textual data into positive, negative, or neutral sentiments. The system leverages Python-based libraries such as Scikit-learn, NLTK, and Transformers to pre- process text, extract features, and apply models in- cluding Support Vector Machine (SVM), Bidirectional Encoder Representations from Transformers (BERT), Linear Regression, and Valence Aware Dictionary and sEntiment Reasoner (VADER). The framework pro- cesses customer reviews from e-commerce platforms (Amazon, Flipkart) and social media (Instagram) using web scraping and provides actionable insights through sentiment summarization and visualizations (bar and pie charts). Experimental results demonstrate BERT’s superior performance with 92.3% accuracy, followed by SVM (85.6%), Linear Regression (81.2%), and VADER (76.8%). The system addresses challenges like sarcasm, class imbalance, and scalability, offering a scalable, user-friendly solution for real-world applications in e- commerce, social media analytics, and brand reputation management. Index Terms—Sentiment Analysis, Machine Learning, Deep Learning, Natural Language Processing, BERT, SVM, VADER, Text Classification, Data Visualization.

Conversational search is the dominant intent of Question Answering, which is achieved through different NLP techniques, Deep Learning models. The advent of Transformer models has been a breakthrough in Natural Language Processing applications, which has attained a benchmark on state of NLP tasks such as question answering. Here we propose a semantic Malayalam Question Answering system that automatically answers the queries related to health issues. The Biomedical Question-Answering, especially in the Malayalam language, is a tedious and challenging task. The proposed model uses a neural network-based Bidirectional Encoder Representation from Transformers (BERT), to implement the question answering system. In this study, we investigate how to train and fine-tune a BERT model for Question-Answering. The system has been tested with our own annotated Malayalam SQUAD form health dataset. In comparing the result with our previous works - Word embedding and RNN based model, identified we find that our BERT model is more accurate than the previous models and achieves an F1 score of 86%.

To address the insufficient expressive capabilities of traditional methods in assessing the development level of rural tourism, this study explores the fusion application of the Bidirectional Encoder Representations from Transformers (BERT) and the Back Propagation (BP) algorithm to enhance the accuracy and comprehensiveness of rural tourism development assessment. Firstly, this study introduces the BERT deep learning model and its applications in natural language processing, alongside the role of the BP algorithm in pattern recognition and predictive analysis. Subsequently, a framework for assessing rural tourism development levels, integrating BERT and the BP algorithm, is proposed. This framework collects multidimensional rural tourism-related data and utilizes the BERT model for sentiment analysis and topic extraction from textual data. Empirical analysis of rural tourism development in a specific region validates the effectiveness of the proposed approach. Experimental results demonstrate: (1) The model achieves an accuracy of 84.33% and an F1 score of 85.33% on the publicly available Laptop dataset, with a processing time of 20 s, significantly outperforming other methods. Compared to traditional approaches, the proposed method accurately captures correlations between textual information and numerical data, thereby enhancing the credibility and accuracy of assessment results. (2) From the ablation study results, it is evident that removing any component from the model leads to performance degradation. Specifically, removing the Bidirectional Gated Recurrent Unit (BiGRU) reduces accuracy and F1 scores to 78.21% and 76.33% on the Laptop dataset, and to 85.10% and 70.45% on the Tourist_F dataset. Removing Text Convolutional Neural Network (CNN) reduces accuracy and F1 scores to 79.34% and 77.56% on the Laptop dataset, and to 86.25% and 72.11% on the Tourist_F dataset. The most significant performance decline occurs upon removing BERT, with accuracy and F1 scores decreasing to 70.14% and 66.43% on the Laptop dataset, and to 78.45% and 60.33% on the Tourist_F dataset. These results indicate that BiGcRU, TextCNN, and BERT each contribute significantly to the model’s performance. This study provides substantial support for advancing sustainable development in rural tourism, offering significant practical and innovative value.

Purpose of research. The purpose of the study is to evaluate certain machine learning models in data processing based on speed and efficiency related to the analysis of sentiment or consumer opinions in business intelligence. To highlight the existing developments, an overview of modern methods and models of sentiment analysis is given, demonstrating their advantages and disadvantages.Materials and methods. In order to improve the semester analysis process, organized using existing methods and models, it is necessary to adjust it in accordance with the growing changes in information flows today. In this case, it is crucial for researchers to explore the possibilities of updating certain tools, either to combine them or to develop them to adapt them to modern tasks in order to provide a clearer understanding of the results of their treatment. We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was conducted using a simple data augmentation approach. This project uses natural language processing (NLP), deep learning, and models such as LSTM, CNN, SVM TF-IDF, Adaboost, Naive Bayes, and then combinations of models.The results of the study allowed us to obtain and verify model results with user reviews and compare model accuracy to see which model had the highest accuracy results from the models and their combination of CNN with LSTM model, but SVM with TF-IDF vectoring was most effective for this unbalanced data set. In the constructed model, the result was the following indexes: ROC AUC - 0.82, precision - 0.92, F1 - 0.82, Precision - 0.82, and Recall - 0.82. More research and model implementation can be done to find a better model.Conclusion. Natural language text analysis has advanced quite a bit in recent years, and it is possible that such problems will be completely solved in the near future. Several different models in ML and CNN with the LSTM model, but SVM with the TF-IDF vectorizer proved most effective for this unbalanced data set. In general, both deep classification algorithm. A combination of both approaches can also learning and feature-based selection methods can be used to solve be used to further improve the efficiency of the algorithm. some of the most pressing problems. Deep learning is useful when the most relevant features are not known in advance, while feature-based

In this paper, a hybrid feature learning approach is proposed to employ aspect sentiment analysis on sentence level through contextual discovery of unstructured text data. The proposed approach joins sentiment lexicon with pre-trained BERT (Bidirectional Encoder Representations from Transformers) word embeddings model for feature deep learning and prediction of context words. In addition, fuzzy ontology reasoning is employed for supporting more in-depth feature extraction through representing semantic knowledge by forming relationships between aspects. Subsequently, the extracted sentiment indicators in online user reviews are classified using Bi-LSTM (Bi-directional Long Short-Term Memory) deep learning model so that the context around words are learned and the corresponding meanings are captured both syntactically and semantically. According to the obtained results, the proposed approach outperforms other related feature learning approaches through improving sentence aspect sentiment analysis and accordingly boosting the overall accuracy of sentiment classification. An average accuracy of 96%, AUC score of 94.5%, and F-score of 95% are achieved by the proposed approach considering five public social media datasets of online reviews. The significance of this study is investigating enrichment of extracted features through using BERT transformer with fuzzy ontology in order to improve the performance of aspect-based sentiment analysis while adding the contextual meanings to the prediction task, and extracting the indirect relationships embedded in social data of user reviews.KeywordsSentiment analysisFeature learningBERTNatural Language ProcessingFuzzy ontology

Text-based sentiment analytics is an important research direction in the field of natural language processing, and it is widely applied in identifying emotional tendencies. The proliferation of e-commerce platforms has underscored the criticality of emotional tendencies and user experiences delineated in product reviews for gauging product quality and user satisfaction. This study presents a developed text-based sentiment analysis system tailored to product reviews on e-commerce platforms. Three distinct text-based sentiment classification models based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) were constructed and implemented. Subsequently, sentiment classification and comparative experiments were conducted utilizing real-world product review data. The experimental outcomes underscore that the BERT model outperforms others in terms of the Area Under the Curve (AUC) metric, yielding superior results in the domain of text sentiment analysis. This paper extensively deliberates on the fusion of deep learning methodologies into sentiment analytics, encapsulating the entire spectrum of data collection, preprocessing, and model analysis, with an overarching goal of providing a reference point for research endeavors in analogous realms.

Sentiment analysis of news media articles is essential for understanding the dynamics of conflict and cooperation in transboundary rivers. However, it is not known which machine learning model(s) can best meet the requirement of sentiment analysis for transboundary rivers. This study presents a comparative examination of ten machine learning models commonly used in the field of text sentiment analysis, including K-Nearest Neighbors, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Decision Tree, Extreme Gradient Boosting, Multilayer Perceptron, Long Short-Term Memory and Bidirectional Encoder Representations from Transformers, for five-class sentiment classification of 9382 news articles (1977–2022) attending to transboundary water conflict and cooperation. By evaluating their performance in terms of accuracy, precision, recall and F1-score, the Bidirectional Encoder Representations from Transformers (BERT) model demonstrated good overall performance and prediction capabilities for news articles with conflictive sentiments. By comparing with the AFINN sentiment dictionary, BERT showed superior performance in the prediction and identification of conflictive sentiment labels. And by validating against historical water events in the three river basins, BERT performed best in the Indus River basin. The findings of this study hold significant implications for government agencies in transboundary rivers, allowing them to promptly assess and respond to public sentiment, thereby preventing water conflict and promoting water cooperation.

Early detection and management of adverse drug reactions (ADRs) is crucial for improving patients’ quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for cancer patients. Recently, an increasing number of patients post their daily experiences to internet community, for example in blogs, where potential ADR signals not captured through routine clinic visits can be described. Therefore, this study aimed to identify patients with potential ADRs, focusing on HFS, from internet blogs by using natural language processing (NLP) deep-learning methods. From 10,646 blog posts, written in Japanese by cancer patients, 149 HFS-positive sentences were extracted after pre-processing, annotation and scrutiny by a certified oncology pharmacist. The HFS-positive sentences described not only HFS typical expressions like “pain" or “spoon nail”, but also patient-derived unique expressions like onomatopoeic ones. The dataset was divided at a 4 to 1 ratio and used to train and evaluate three NLP deep-learning models: long short-term memory (LSTM), bidirectional LSTM and bidirectional encoder representations from transformers (BERT). The BERT model gave the best performance with precision 0.63, recall 0.82 and f1 score 0.71 in the HFS user identification task. Our results demonstrate that this NLP deep-learning model can successfully identify patients with potential HFS from blog posts, where patients’ real wordings on symptoms or impacts on their daily lives are described. Thus, it should be feasible to utilize patient-generated text data to improve ADR management for individual patients.