Natural Language Processing Techniques Research Articles

Governance in multi-system transitions: A new methodological approach for actor involvement in policy making processes

Multi-system interactions associated with the decarbonisation of energy and mobility systems represent a complex phenomenon in the acceleration phase of net-zero transitions. In this paper, we present a novel methodological approach to examine actor involvement in the governance of multi-system transitions, with a focus on the UK's net-zero energy-mobility transitions from 2008 to 2021. Utilising Named Entity Recognition (NER), a natural language processing technique, we systematically map actors and their interactions within policy consultations and how these have changed over time. Our analysis differentiates between single-system and multi-system policy making processes; identifies weak and strong links among actors as two types of multi-system interactions; categorises actors into business, policy, academia, and society groups; and examines the evolution of engagement across multiple governance levels. Our findings indicate an increasing trend of multi-system interactions, suggesting the UK's progression towards the acceleration phase of net-zero transitions. Our analysis further reveals the predominance of policy actors, particularly from the national level, in governing such multi-system transitions processes, followed by business actors. Despite some limitations, our approach offers a scalable method for analysing large volumes of text, providing valuable insights into the governance dynamics of multi-system transitions. We conclude with implications for policy making and offer suggestions for future research, emphasising the importance of understanding actor involvement and political contestations around net-zero trajectories for ensuring the achievement of sustainability goals.

Automated System to Capture Patient Symptoms From Multitype Japanese Clinical Texts: Retrospective Study.

Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system. We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration. Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records. Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents.

Corporate environmental, social, and governance information disclosure and audit governance in the context of green development

The purpose of this paper is to investigate the relationship between Environmental, Social, and Governance (ESG) disclosure and audit governance. The objective is to assess the impact of auditor team independence, auditor professional quality, and transparency of audit procedures on the quality of information disclosure, and to elucidate the role of these factors in advancing sustainable development. Research methodologies include the optimization of information disclosure text analysis using natural language processing (NLP) and text generation techniques, complemented by empirical analysis. Key findings indicate that the maximum error between NLP and manual annotation in the proportion of positive emotions in information disclosure is 0.03, and 0.06 in audit governance. The Rouge index score surpasses the 0.5 threshold, while the Perplexity value varies across three experiments, though remaining generally low. Deep learning model results reveal that auditor team independence, professional quality, and procedural transparency significantly enhance the quality of information disclosure. These findings offer crucial insights for enterprises, regulators, and investors, affirming the rationality and stability of the model, and providing valuable input for future green development strategies and decision-making processes.

PageRank Algorithm-Based Recommendation System for Construction Safety Guidelines

The construction industry faces significant challenges with frequent accidents, largely due to the inefficient use of safety guidelines. These guidelines, which are often text and figure heavy, demand substantial human effort to identify the most relevant items for specific tasks and conditions. Additionally, the guidelines contain both central and peripheral elements, and central items are critical yet difficult to identify without extensive domain knowledge. This study proposes a novel recommendation framework to enhance the usability of these safety guidelines. By leveraging natural language processing (NLP) and knowledge graph (KG) modeling techniques, unstructured safety texts are transformed into a structured, interconnected KG. The PageRank and Louvain Clustering algorithm is then employed to rank guidelines by their relevance and importance. A case study on “High-rise Building Construction (General) Safety and Health Guidelines”, using ‘scaffolding’ as the keyword, demonstrates the framework’s effectiveness in improving retrieval efficiency and practical application. The analysis highlighted key clusters such as ‘fall’, ‘drop’, and ‘scaffolding’, with critical safety measures identified through their interconnections. This research not only overcomes the fragmentation of safety management documents but also contributes to advancing hazard analysis and risk prevention practices in construction management.

Vulnerability detection based on transformer and high‐quality number embedding

SummarySoftware vulnerability detection is an important problem in software security. In recent years, deep learning offers a novel approach for source code vulnerability detection. Due to the similarities between programming languages and natural languages, many natural language processing techniques have been applied to vulnerability detection tasks. However, specific problems within vulnerability detection tasks, such as buffer overflow, involve numerical reasoning. For these problems, the model needs to not only consider long dependencies and multiple relationships between statements of code but also capture the magnitude property of numerical literals in the program through high‐quality number embeddings. Therefore, we propose VDTransformer, a Transformer‐based method that improves source code embedding by integrating word and number embeddings. Furthermore, we employ Transformer encoders to construct a hierarchical neural network that extracts semantic features from the code and enables line‐level vulnerability detection. To evaluate the effectiveness of the method, we construct a dataset named OverflowGen based on templates for buffer overflow. Experimental comparisons on OverflowGen with a well‐known static vulnerability detector and two state‐of‐the‐art deep learning‐based methods confirm the effectiveness of VDTransformer and the importance of high‐quality number embeddings in vulnerability detection tasks involving numerical features.

Bringing Artificial Intelligence to Research Analytics: Research Highlighter-MatchMaker

It is more significant to identify accurate grant opportunities given the increasing challenges in securing grants these days. Combining Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques, we have developed Research Highlighter-MatchMaker to facilitate the grant application process. The tool aims to provide an overview of research strength and to recommend matched funding opportunities as well as potential collaborators for researchers based on their research track records. It has the potential to expand to provide matching for on-campus researchers and industrial partners. It will also include a chatbot to facilitate the research administration infrastructure and process on campus.

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study.

According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications. Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies? We used X's application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non-English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability. Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health. This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

Using natural language processing approaches to characterize professional experiences of child welfare workers

High turnover rates among child welfare workers, including caseworkers and supervisors, are associated with negative attitudes and perceptions about work (NAPW). Our study aimed to (a) explore how child welfare workers’ demographic characteristics, professional attributes, and their work narratives are related to NAPW and (b) extract themes from these narratives to understand their professional needs and concerns. In 2022, a statewide online survey was administered to active child welfare caseworkers and supervisors in a large Midwestern state. This survey, combining standardized scales and an open-ended question about work experiences, yielded numeric (N = 551 survey respondents) and text data (N = 2,193 words). We analyzed the numeric data using general linear modeling and the text data using natural language processing (NLP) techniques. Results showed that workers associated with higher NAPW were female, younger, from racial and ethnic minority backgrounds, less tenured, and more likely to be employed in public agencies and to work longer hours. They are predominantly caseworkers rather than supervisors. Worker role and agency affiliation had the largest effect sizes. NLP analysis showed that workers with more NAPW frequently used third-person pronouns and emotionally negative language, suggesting a disconnection from colleagues and job dissatisfaction. Caseworkers often used first-person singular pronouns, indicating feelings of isolation. Two key themes emerged: a) the need for better agency support during home visits and b) concerns over traumatic experiences and ineffective trauma-informed practices. The findings suggest that future interventions should prioritize the well-being of caseworkers in public agencies and consider language patterns in workers’ narratives to understand their vulnerability to NAPW.

Development of a Natural Language Processing (NLP) model to automatically extract clinical data from electronic health records: results from an Italian comprehensive stroke center

IntroductionData collection often relies on time-consuming manual inputs, with a vast amount of information embedded in unstructured texts such as patients’ medical records and clinical notes. Our study aims to develop a pipeline that combines active learning (AL) and NLP techniques to enhance data extraction in an acute ischemic stroke cohort. Materials and methodsConsecutive acute ischemic stroke patients who received reperfusion therapies at IRCCS Humanitas Research Hospital were included. The Italian NLP Bidirectional Encoder Representations from Transformers (BERT) model was trained with AL to automatically extract clinical variables from electronic health text. Simulated active learning performances were evaluated on a set of labels representing patients’ comorbidities, comparing Bayesian Uncertainty Sampling by Disagreement (BALD) and random text selection. Prognostic models predicting patients’ functional outcomes using Gradient Boosting were trained on manually labelled and semi-automatically extracted data and their performance was compared. ResultsThe active learning process initially showed null performance until around 20% of texts were labelled, possibly due to root layers freezing in the BERT model, yet overall, active learning improves model learning efficiency across most comorbidities. Prognostic modelling showed no significant difference in performance between models trained on manually labelled versus semi-automatically extracted data, indicating effective prediction capabilities in both settings. ConclusionsWe developed an efficient language model to automate the extraction of clinical data from Italian unstructured health texts in a cohort of ischemic stroke patients. In a preliminary analysis, we demonstrated its potential applicability for enhancing prediction model accuracy.

Cascaded Adaptive Dilated Temporal Convolution Network-Based Efficient Sentiment Analysis Model from Social Media Posts

Sentimental analysis is one of the most complicated tasks in text mining. It is the process of converting unstructured text to structural text to identify the meaningful pattern of data. Different kinds of sentiments in social media posts are classified into positive and negative. Natural Language Processing (NLP) plays an imperative task in examining sentiments on social media. Sentimental analysis techniques frequently use text data to help to monitor customer feedback about products and understand the needs of customers in the field of business. The text data are gathered from social media comments that lead to several drawbacks such as spelling errors, noise, and unstructured data, and some of the data are in abbreviation form in which abbreviations are hard to understand from the text data. Therefore, the innovative sentiment analysis method is implemented to identify the kinds of sentiments from the collected social media posts. At first, the text data are assembled from the social media posts. The collected text data are subjected to text preprocessing techniques to improve the data quality. NLP techniques such as a bag of n-grams, glove embedding, and Bidirectional Encoder Representations from Transformers (BERT) are applied to separate the features from the text data. The removed features from these techniques are given to the fusion of the optimal feature stage, where the weight optimization takes place via the Mutated Random Parameter-based Serval Optimization Algorithm (MRP-SOA). The attained optimal weighted fused features contain the most relevant information, and it is fed to the classifier for analyzing the sentiments. For analyzing the sentiments, the Cascaded Adaptive Dilated Temporal Convolutional Network (CADTCN) is utilized, and the parameters from ADTCN are optimized with the same to get higher classification outcomes. The analysis outcomes are compared with the traditional sentiment analysis models in terms of various metrics to show the success of the advanced design.

The Analysis of Tourism Attitudes using Natural Language Processing Techniques: A Case of Malaysian Tourists

The spread of COVID-19 has had a significant impact on all facets of the global tourism sector, particularly in Thailand, one of the world’s leading travel destinations. At the height of the epidemic, many countries imposed a nationwide lockdown, prohibiting all citizens from leaving the country and all foreign tourists from entering. This led to a global shutdown that significantly affected the daily lives of billions of people and seriously impacted the travel and tourism industry. After a two-year hiatus due to the epidemic, the situation eased and the lockdown restrictions were lifted. An interesting question is how visitors’ attitudes and preferences changed when compared to the time before the outbreak. This study attempts to answer this question by focusing on Malaysian visitors’ attitudes and perceptions toward destinations in southern Thailand. The study examines the perceptions of Malaysian Twitter (now X) users from three areas in Malaysia; Kedah, Perlis, and Kuala Lumpur, by employing Natural Language Processing (NLP) techniques such as sentiment analysis and topic modeling. Then, tweet data before and after the lockdowns were gathered, analyzed, and compared. For sentiment analysis, it was found that, when neutral tweets were disregarded, results both before and after the COVID-19 lockdowns revealed that the attitudes conveyed by Malaysian tourists were overall positive especially a territory and a state that are more far away from Thailand, i.e., Kuala Lumpur and Kedah. The results from the topic modeling analysis showed a meaningful distinction between before and after the COVID-19 lockdowns. Practical suggestions are offered for tourism policymakers to identify and address both the strengths and weaknesses of tourism development in Southern Thailand.

Maximizing Profitability and Occupancy: An Optimal Pricing Strategy for Airbnb Hosts Using Regression Techniques and Natural Language Processing

In the competitive landscape of Airbnb hosting, optimizing pricing strategies for properties is a complex challenge that requires revenue maximization with high occupancy rates. This research aimed to introduce a solution that leverages big data and machine learning techniques to help hosts improve their property’s market performance. Our primary goal was to introduce a solution that can augment property owners’ understanding of their property’s market value within their urban context, thereby optimizing both the utilization and profitability of their listings. We employed a multi-faceted approach with diverse models, including support vector regression, XGBoost, and neural networks, to analyze the influence of factors such as location, host attributes, and guest reviews on a listing’s financial performance. To further refine our predictive models, we integrated natural language processing techniques for in-depth listing review analysis, focusing on term frequency-inverse document frequency (TF-IDF), bag-of-words, and aspect-based sentiment analysis. Integrating such techniques allowed for in-depth listing review analysis, providing nuanced insights into guest preferences and satisfaction. Our findings demonstrated that AirBnB hosts can effectively utilize both state-of-the-art and traditional machine learning algorithms to better understand customer needs and preferences, more accurately assess their listings’ market value, and focus on the importance of dynamic pricing strategies. By adopting this data-driven approach, hosts can achieve a balance between maintaining competitive pricing and ensuring high occupancy rates. This method not only enhances revenue potential but also contributes to improved guest satisfaction and the growing field of data-driven decisions in the sharing economy, specially tailored to the challenges of short-term rentals.

Clearing Up Confusion: The Effect of Outlier Similarity on IPO Underpricing

Prior research demonstrates that audiences tend to converge in their valuations of firms similar to preexisting category prototypes or exemplars. Much less is known of the influence of salient outliers, specific firms that receive market-wide attention due to their extreme, ambiguous performance, on audiences’ valuations. We argue that outlier similarity, by contrast with prototype similarity, leads to divergent valuations among individual investors. We explore this insight in the context of initial public offerings (IPOs). In this context, converging valuations among investors lead to limited information asymmetry concerns and hence reduced underpricing on the first day of trading of an issuing firm. Hence, we expect that prototype similarity leads to lower underpricing while outlier similarity leads to higher underpricing. We test our hypotheses using a sample of 2,488 U.S. IPOs from 1996 to 2015, measuring prototype and outlier similarity through a natural language processing technique applied to nearly 160,000 financial documents. We find that in low-tech industries, where prototypes are informative about category members, prototype similarity reduces underpricing, but not in high-tech industries. Additionally, we find that outlier similarity increases underpricing, especially for more recent outliers. This paper contributes to the literature on market valuation and market categories, and advances research on meaning and culture using new text-based computational methods.

Sentiment Mining in E-Commerce

Sentiment analysis is crucial for comprehending customer feedback and enhancing workplace culture, as well as improving products and services. By employing natural language processing (NLP) techniques to meticulously analyze this feedback, organizations can identify specific areas that require improvement, address employee issues, and cultivate a positive work environment. These deep learning models powered by NLP offer invaluable tools for HR and sales departments in the e-commerce sector, enabling them to track sentiment trends among employees and users over time and implement targeted interventions. Focusing on the e-commerce industry, this study employs NLP-driven deep learning methodologies to analyze both employee and user feedback, with the objective of identifying underlying sentiments. The proposed framework leverages these advanced techniques to categorize user feedback into positive, negative, or neutral sentiments. This approach aims to develop a robust and effective system for sentiment analysis, providing significant insights that can help drive organizational improvements and enhance customer satisfaction. The key steps of this framework include data collection, NLP-enhanced feature extraction, sentiment detection, and final classification using finite-state automata. The effectiveness of this NLP-centric approach was tested on diverse datasets of customer feedback collected from an e-commerce industry. Evaluation metrics such as accuracy, precision, and recall were utilized to assess the performance of the system. The results demonstrate the effectiveness of the proposed framework, achieving a 93.75% accuracy rate and surpassing existing benchmark methods. The outcomes of this study are particularly consequential for the e-commerce sector, offering them a strategic advantage in refining their product portfolios and cultivating a more dynamic workplace culture

Rethinking Cultural Ecosystem Services in Urban Forest Parks: An Analysis of Citizens’ Physical Activities Based on Social Media Data

Urban forest parks play a vital role in promoting physical activities (PAs) and providing cultural ecosystem services (CESs) that enhance citizens’ well-being. This study aims to reevaluate CESs by focusing on the physical activity experiences of park visitors to optimize park management and enhance citizen satisfaction. This study utilized social media data and employed natural language processing techniques and text analysis tools to examine experiences related to physical activities in Beijing Olympic Forest Park, Xishan Forest Park, and Beigong Forest Park. A specialized sports activity dictionary was developed to filter and analyze comments related to PA, emphasizing the impact of natural environments on enjoyment and participation in PA. The importance–performance analysis (IPA) method was used to assess the service characteristics of each park. The findings reveal that urban forest parks are highly valued by citizens, particularly for their natural landscapes, leisure activities, and the emotional fulfillment derived from PA, with 82.58% of comments expressing positive sentiments. Notably, appreciation for natural landscapes was exceptionally high, as evidenced by the frequent mentions of key terms such as ‘scenery’ (mentioned 2871 times), ‘autumn’ (mentioned 2314 times), and ‘forest’ (mentioned 1439 times), which significantly influence park usage. However, 17.11% of the reviews highlighted dissatisfaction, primarily with the management of facilities and services during sports and cultural activities. These insights underscore the need for performance improvements in ecological environments and sports facilities. This study provides a novel perspective on assessing and optimizing urban forest parks’ functions, particularly in supporting active physical engagement. The rich CESs offered by these parks enhance physical activity experiences and overall satisfaction. The findings offer strategic insights for park managers to better meet citizens’ needs and improve park functionality.

Analysing Psychological Sentiment Prediction Across Modalities: Harnessing Emotion Datasets within Natural Language Processing (NLP)

Studying human emotions and feelings is a crucial element in the field of psychology, having significant implications such as evaluating mental health and improving human-computer interactions. Recently, there has been a rise in interest in examining how psychological sentiment can be predicted through various mediums, including text, audio, video, and physiological signals. By utilizing advancements in Natural Language Processing (NLP) and analysing multimodal data, this research delves into incorporating emotion datasets into NLP frameworks to improve psychological sentiment prediction. This presents present an applicability of some of the NLP techniques to predicts the Psychological Sentiment such as Deep Generating adversarial networks (D-GANs), Long short-term memory (LSTM) and gated recurrent unit (GRU). The sentimental analysis performed by the considered algorithms are implemented in python and the parameters which are considered for the results evaluation are model loss, confusion matrix, accuracy, precision, recall and f1-score. Our goal with this analysis is to offer a deeper understanding of the existing methodologies and future scope of growth in psychological sentiment prediction of research in the field of NLP.

Text-to-Model Transformation: Natural Language-Based Model Generation Framework

System modeling language (SysML) diagrams generated manually by system modelers can sometimes be prone to errors, which are time-consuming and introduce subjectivity. Natural language processing (NLP) techniques and tools to create SysML diagrams can aid in improving software and systems design processes. Though NLP effectively extracts and analyzes raw text data, such as text-based requirement documents, to assist in design specification, natural language, inherent complexity, and variability pose challenges in accurately interpreting the data. In this paper, we explore the integration of NLP with SysML to automate the generation of system models from input textual requirements. We propose a model generation framework leveraging Python and the spaCy NLP library to process text input and generate class/block definition diagrams using PlantUML for visual representation. The intent of this framework is to aid in reducing the manual effort in creating SysML v1.6 diagrams—class/block definition diagrams in this case. We evaluate the effectiveness of the framework using precision and recall measures. The contribution of this paper to the systems modeling domain is two-fold. First, a review and analysis of natural language processing techniques for the automated generation of SysML diagrams are provided. Second, a framework to automatically extract textual relationships tailored for generating a class diagram/block diagram that contains the classes/blocks, their relationships, methods, and attributes is presented.

Textual analysis and gold futures price forecasting: Evidence from the Chinese market

This paper examines the predictive capacity of online news on the gold futures prices. The empirical results derived from the Chinese market demonstrate that the textual features extracted through natural language processing techniques contain complementary predictive content for gold futures prices, which enhance the 1-day ahead prediction accuracy across different machine learning methods and train-test sets.

The Use of Natural Language Processing Methods in Reddit to Investigate Opioid Use: Scoping Review.

The growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis. We aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use. We systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations. In total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines. This scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Advanced Sarcasm Detection in Newspaper Headlines: A Comparative Study of Machine Learning Approaches

This study presents a comprehensive analysis of sarcasm detection in newspaper headlines using various machine learning techniques. We explore the effectiveness of logistic regression, Naïve Bayes (multinomial and Gaussian), random forest, and support vector machine (SVM) models in identifying sarcastic content within the concise format of news titles. Our research addresses the unique challenges posed by the brevity and impactful language characteristic of headlines. The study utilizes a curated dataset of sarcastic and non-sarcastic headlines, employing natural language processing techniques for preprocessing and feature extraction. Performance evaluation metrics include accuracy, precision, recall, and F1-score. Results indicate that the random forest model outperforms other approaches, achieving 94% accuracy in sarcasm detection. This research contributes to the growing field of sentiment analysis and offers insights into the nuanced task of decoding sarcasm in condensed textual formats. KEYWORDS: Random Forest, Support Vector Machine (SVM), TF-IDF, Natural Language Processing (NLP), Subtle Sarcasm