Natural Language Processing Approaches Research Articles

Measuring the Social Acceptability of Infrastructure Investments: A Natural Language Processing Approach

Measuring the Social Acceptability of Infrastructure Investments: A Natural Language Processing Approach

Advanced Cyber Attack Detection Using Generative Adversarial Networks and NLP

A key difficulty in the ever-changing cybersecurity scene is the detection of sophisticated cyber-attacks. Because new threats are so much more sophisticated and difficult to detect, traditional tactics typically fail. A new technique to improving cyber-attack detection skills is explored in this study. It uses Generative Adversarial Networks (GANs) and Natural Language Processing (NLP). Using GANs' realistic data generation capabilities, possible attack paths are simulated, creating a strong dataset for training detection systems. At the same time, natural language processing (NLP) methods are used to decipher the mountain of textual information produced by cyberspace, including incident reports, communication patterns, and logs. Our approach is based on building a fake dataset using GANs that mimics the features of advanced cyberattacks. A detection model is then trained using this dataset. Simultaneously, we improve the detection model's capacity to spot intricate and nuanced assault patterns by processing and analysing text-based data using natural language processing approaches. We use a benchmark cybersecurity dataset to test the integrated method. The experimental findings show that our GAN-NLP based detection system outperforms existing systems, which have an average accuracy of 85.3%, by a wide margin. It achieves a recall of 93.2%, precision of 92.5%, and accuracy of 94.7%. These findings prove that GANs and NLP work well together to identify complex cyberattacks. Finally, GANs and NLP together provide a potent instrument for better cyber-attack detection. A scalable solution that can adapt to the ever-changing nature of cyber threats is offered by this integrated approach, which also increases detection accuracy and efficiency. Improving the models and investigating their use in a real-world cybersecurity setting will be the primary goals of future research.

Natural Language Processing Approaches in Industrial Maintenance: A Systematic Literature Review

Industrial maintenance plays a crucial role in manufacturing by significantly reducing machine failure time and minimizing costs, especially in the revolution of Industry 4.0. Consequently, researchers and industrial engineers have continuously focused on this area. Manufacturing companies possess extensive maintenance reports or logs containing valuable textual information, which offers a new avenue for exploring effective industrial maintenance methods. Natural Language Processing (NLP), a subfield of Artificial Intelligence, has demonstrated remarkable potential in analyzing maintenance reports and achieving promising results in various tasks. This paper presents a comprehensive systematic literature review that specifically concentrates on the applications of NLP approaches employed in the field of industrial maintenance. Additionally, this review analyzed the datasets utilized in previous studies and the evaluation measures adopted, which can serve as a valuable resource for other researchers seeking potential solutions in maintenance. Furthermore, the paper discusses the challenges encountered in applying NLP to industrial maintenance and outlines future research directions in this domain. By conducting this systematic literature review, we provide a comprehensive understanding of the current state of NLP applications in industrial maintenance, identify gaps in the existing literature, and guide future research efforts in leveraging NLP techniques for enhanced maintenance practices.

An Analysis of the News Coverage of Fintech in Africa: A Natural Language Processing Approach

An Analysis of the News Coverage of Fintech in Africa: A Natural Language Processing Approach

Identifying Non-functional Requirements from Unconstrained Documents using Natural Language Processing and Machine Learning Approaches

Identifying Non-functional Requirements from Unconstrained Documents using Natural Language Processing and Machine Learning Approaches

Evolução do uso de agentes conversacionais na educação empresarial: Passado, presente e futuro

Abstract Purpose: The primary objective of this article is to answer the proposed research questions by analyzing the publications on artificial intelligence (AI), education, business, and conversational agents (CAs) or chatbots within the Scopus database to identify the most frequent terms used by scholars over the years, as well as to classify the research evolution based on the most important thematic trends. Originality/value: The study contributes to the literature by offering insights into the thematic landscape of AI-related research across multiple domains. By employing a comprehensive methodology, the article provides a nuanced understanding of the intersection between AI, education, business, and CAs. Design/methodology/approach: The methodology comprises two main components. Firstly, topic modeling is applied by the year of publication to summaries of articles meeting the search criteria. Secondly, grounded theory coding was used to categorize generated themes into more meaningful classifications. This dual approach ensures a rigorous data analysis and facilitates the identification of overarching thematic trends. Findings: The results reveal five thematic trends investigated in the analyzed publications: 1. student-centered learning in higher education; 2. interactive methods using the natural language processing (NLP) approach; 3. technological solutions and ChatGPT in a university context; 4. enhancing education through intelligent platforms; and 5. challenges for social and academic integration of AI tools. Additionally, the study proposes a research agenda with some questions as future avenues of inquiry.

Understanding older people's voice interactions with smart voice assistants: a new modified rule-based natural language processing model with human input.

The COVID-19 pandemic has expedited the integration of Smart Voice Assistants (SVA) among older people. The qualitative data derived from user commands on SVA is pivotal for elucidating the engagement patterns of older individuals with such systems. However, the sheer volume of user-generated voice interaction data presents a formidable challenge for manual coding. Compounding this issue, age-related cognitive decline and alterations in speech patterns further complicate the interpretation of older users' SVA voice interactions. Conventional dictionary-based textual analysis tools, which count word frequencies, are inadequate in capturing the evolving and communicative essence of these interactions that unfold over a series of dialogues and modify with time. To address these challenges, our study introduces a novel, modified rule-based Natural Language Processing (MR-NLP) model augmented with human input. This reproducible approach capitalizes on human-derived insights to establish a lexicon of critical keywords and to formulate rules for the iterative refinement of the NLP model. English speakers, aged 50 or older and residing alone, were enlisted to engage with Amazon Alexa™ via predefined daily routines for a minimum of 30 min daily spanning three months (N = 35, mean age = 77). We amassed time-stamped, textual data comprising participants' user commands and responses from Alexa™. Initially, a subset constituting 20% of the data (1,020 instances) underwent manual coding by human coder, predicated on keywords and commands. Separately, a rule-based Natural Language Processing (NLP) methodology was employed to code the identical subset. Discrepancies arising between human coder and the NLP model programmer were deliberated upon and reconciled to refine the rule-based NLP coding framework for the entire dataset. The modified rule-based NLP approach demonstrated notable enhancements in efficiency and scalability and reduced susceptibility to inadvertent errors in comparison to manual coding. Furthermore, human input was instrumental in augmenting the NLP model, yielding insights germane to the aging adult demographic, such as recurring speech patterns or ambiguities. By disseminating this innovative software solution to the scientific community, we endeavor to advance research and innovation in NLP model formulation, subsequently contributing to the understanding of older people's interactions with SVA and other AI-powered systems.

A natural language processing approach to skill identification in the job market

The dynamic nature of the job market, with its constant growth and evolving demands, makes the assessment of skills needed for a particular job increasingly difficult, time-consuming, and tedious. This necessitates an automated approach to identify the required skills for job seekers and recruiters. This study aimed to utilize machine learning (ML) and deep learning algorithms to streamline skill identification from technology-related job descriptions. We tested the viability of the Bidirectional Encoder Representation of Transformers (BERT) language model to complete this multi-class, multi-label classification task. We hypothesized that BERT, utilizing an NLP transformer, would predict job-required skills more accurately than traditional statistical ML models. To test this, we utilized a dataset comprised of job descriptions with varying skills. We experimented with two preprocessing approaches, K-Means Clustering and Latent Dirichlet Allocation, which are both statistical ML models meant to cluster the job descriptions. We then compared the performance of the BERT tokenizer to a combination of text-to-number tokenization techniques (Term Frequency - Inverse Document Frequency and Word2Vec) and supervised classifiers (K-Nearest Neighbors, Random Forest, and Multi-Layer Perceptron). The NLP transformer-based BERT model achieved the highest accuracy. We conclude that while traditional ML models provide some insights, advanced models like BERT show potential for more accurate skill prediction since the NLP transformer model we tested outperformed the ML models. However, the complexity of real-world data necessitates further refinement and development of these technologies. Our study shows that advanced deep learning-based models have the potential to enhance the job market’s efficiency by automating skill identification.

Emotion Classification on Social Media Comments Using Categorical Feature Extraction Along With the Bidirectional Encoder-based Recurrent Neural Network Classification

All across the world, social media is one of the most widely used platforms for information exchange. Comments on relevant information might be made in response to a video or any other piece of information. A remark may include an emotion that may be recognized by an automated recognition system. On Facebook, Twitter, and YouTube comments, we performed studies to determine their emotional categorization. A set of comments is gathered and manually classified using six fundamental emotion labels (happy, sad, angry, surprised, disgust, and fear) and one neutral label, with each emotion label representing a different emotion category. A prominent approach in natural language processing (NLP), deep learning has been used in a wide range of categorization applications. This procedure begins by preprocessing the input data with normalization, followed by categorizing characteristics in feature extraction utilizing the Linguistic and word count analysis (LIWC). Finally, for the categorization stage, the classify features might be supplied. Finally, for categorizing emotions, the Bidirectional Encoder based recurrent neural network classification approach is used. The studies have been carried out with the use of typical social media data that has been acquired from the kaggle data repository. The findings show that the suggested model outperforms all other existing mechanisms in terms of overall performance.

Recommender system based on Convolutional Recurrent Deep Learning for protein-drug interaction prediction

The tremendous growth the pharmaceutical field witnessed especially concerning drug-development, caused drug-related experiments such as those concerned with estimating their interactions with targets harder to conduct. This issue led researchers to transition from vivo and vitro to selico experimenting methods, which deploy artificial intelligence (AI) techniques such as recommender systems (RS) to solve problems at hand. However, most RSs depend on global features such as indices, which render them incapable of fully understanding the behaviors of individuals. In this paper, we introduce our Convolutional Recurrent Deep Learning model-based RS approach, or ‘RSCRDL’, which tackles the interaction prediction problem from a natural language processing approach’s (NLP) point of view. RSCDRL processes the textual representations of both protein sequences and drugs in order to learn the implicit features of the tokens forming them, rendering it able to process unseen and non-reactive individuals indirectly. We also present a greedier variant of RSCRDL that can exploit additional drugs explicit features for better predictive abilities. Experiments performed on two real-world datasets show significant outperformance achieved by our models over two general-purpose state-of-the-art RSs, while other experiments conducted on orphan protein sequences helped deduce drug candidates that are assumed to be capable of inhibiting their activities.

Uncovering Sustainability Insights from Amazon’s Eco-Friendly Product Reviews for Design Optimization

This research investigates consumer reviews of eco-friendly products on Amazon to uncover valuable sustainability insights that can inform design optimization. Using natural language processing (NLP) techniques, including sentiment analysis, key terms extraction, and topic modeling, this research reveals diverse perspectives related to sustainability aspects in eco-friendly products. Innovatively, we integrate the NLP approach with correspondence analysis (CA) to understand consumer sentiments and preferences related to sustainability aspects. Leveraging CA, we visualize the interplay between eco-friendly product features and consumer sentiments, revealing underlying relationships and patterns. The CA biplot showcases the alignment of specific sustainability attributes with consumer satisfaction, highlighting which sustainability aspects hold greater influence over overall product ratings. As sustainability becomes an increasingly crucial aspect of consumer choices, our paper emphasizes the significance of a multidimensional approach that embraces both qualitative and quantitative insights. By blending CA with consumer reviews, we equip designers and stakeholders with an innovative and comprehensive toolkit to enhance sustainable design practices, paving the way for more informed and effective product development strategies in the realm of eco-friendliness.

Surveying the landscape of text summarization with deep learning: A comprehensive review

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting. This survey aims to provide a comprehensive review of existing techniques, evaluation methodologies, and practical applications of automatic text summarization.

Natural language processing for detecting adverse drug events: A systematic review protocol

Background Detecting Adverse Drug Events (ADEs) is an emerging research area, attracting great interest in the research community. Better anticipatory management of predisposing factors has considerable potential to improve outcomes. Automatic extraction of ADEs using Natural Language Processing (NLP) has a great potential to significantly facilitate efficient and effective distillation of such knowledge, to better understand and predict risk of adverse events. Methods This systematic review follows the six-stage including the literature from 6 databases (Embase, Medline, Web Of Science, ACM Guide to Computing Literature, IEEE Digital Library and Scopus). Following the title, abstract and full-text screenings, characteristics and main findings of the included studies and resources will be tabulated and summarized. The risk of bias and reporting quality was assessed using the PROBAST tool 1 . Results We developed our search strategy and collected all relevant publications. As of October 2023, we have completed the first two stages of the systematic review. We identified 178 studies for inclusion through the academic literature search (where data was extracted from 118 papers). Further refinement of the eligibility criteria and data extraction has been ongoing since August 2022. Conclusion In this systematic review, we will identify and consolidate information and evidence related to the use and effectiveness of existing NLP approaches and tools for automatically detecting ADEs from free text (discharge summaries, General Practitioner notes, social media, etc.). Our findings will improve the understanding of the current landscape of the use of NLP for extracting ADEs. It will lead to better anticipatory management of predisposing factors with the potential to improve outcomes considerably. Our results will also be valuable both to NLP researchers developing methods to extract ADEs and to translational/clinical researchers who use NLP for this purpose and in healthcare in general.

Finding social need-les in a haystack: ascertaining social needs of Medicare patients recorded in the notes of care managers

BackgroundUnmet social needs may impair health and access to health care, and intervening on these holds particular promise in high-risk patient populations, such as those with multiple chronic conditions. Our objective was to identify social needs in a patient population at significant risk—Medicare enrollees with multiple chronic illnesses enrolled in care management services—and measure their prevalence prior to any systematic screening.MethodsWe partnered with Renova Health, an independent Medicare Chronic Care Management (CCM) provider with patients in 10 states during our study period (January 2017 through August 2020). Our data included over 3,000 Medicare CCM patients, representing nearly 20,000 encounters. We used a dictionary-based natural language processing approach to ascertain the prevalence of six domains of barriers to care (food insecurity, housing instability, utility hardship) and unmet social needs (health care affordability, need for supportive services, transportation) in notes taken during telephonic Medicare CCM patient encounters.ResultsBarriers to care, specifically need for supportive services (2.4%) and health care affordability (0.8%), were the most prevalent domains identified. Transportation as a barrier to care came up relatively less frequently in CCM encounters (0.1%). Unmet social needs were identified at a comparatively lower rate, with potential housing instability (0.3%) flagged most followed by potential utility hardship (0.2%) and food insecurity (0.1%).ConclusionsThere is substantial untapped opportunity to systematically screen for social determinants of health and unmet social needs in care management.

ML and Natural Language Processing : Cyberbullying Detection System for Safer and Culturally Adaptive Digital Communities

Abstract Cyberbullying has become a ubiquitous menace in our digitally connected society, requiring strong detection and classification systems. This study presents a multi-tiered system that reliably detects and classifies instances of cyberbullying on a variety of platforms by utilising cutting-edge machine learning and natural language processing approaches. Our algorithm, which was trained on a wide range of datasets, shows excellent accuracy in differentiating between instances of cyberbullying and non-bullying situations while taking linguistic and cultural quirks into account. Furthermore, our flexible system guarantees applicability by adjusting to changing cyberbullying patterns. By promoting safer and more inclusive digital communities, our research helps to design proactive treatments that lessen the effects of online harassment. This study introduces a robust multi-tiered system designed for the detection and classification of cyberbullying across diverse digital platforms. Leveraging state-of-the-art machine learning and natural language processing techniques, our algorithm, trained on extensive datasets, exhibits exceptional accuracy in distinguishing cyberbullying instances from non-bullying scenarios while accommodating linguistic and cultural nuances. The system’s adaptability to evolving cyberbullying patterns ensures continued efficacy. By fostering safer and more inclusive online environments, our research contributes to proactive measures and mitigates the impact of digital harassment.

HTA346 The Use of Real-World Evidence in Health Technology Assessment Decisions in Sweden and the UK: A Natural Language Processing (NLP) Approach

HTA346 The Use of Real-World Evidence in Health Technology Assessment Decisions in Sweden and the UK: A Natural Language Processing (NLP) Approach

Further Validation of a Self‐administered Smartphone‐based Verbal Learning and Memory Assessment

AbstractBackgroundThe International Shopping List test (ISLT) is a verbal word list learning test set within a realistic shopping list context, supporting ease of cultural adaptation, and addressing content validity and patient relevance. Improvements to and the wide adoption of natural language processing (NLP) software has enabled the possibility for self‐administered tests of verbal learning. LilaTM a self‐administered, smartphone‐application (app) was developed, using the virtual assistant and NLP. We have previously reported acceptability and usability data from qualitative analysis of concept elicitation interviews. We now seek to provide preliminary validation data around its use.MethodA convenience sample of healthy young adults (n = 40), M(SD)age = 35 (16), were invited to install and complete one assessment of the LilaTM app. A usability survey was also administered to explore participants’ experiences. The sample was predominantly female (70%) and of Asian ethnicity (62.5%). Data were collected in Australia, using an English word pool developed for the USA.ResultAcceptability was generally high with N = 38 completing immediate recall rounds and N = 32 completing the delayed recall round. Eight‐two percent found it extremely or somewhat easy to hear and understand what items were on the shopping list and 95% indicated that it was extremely or somewhat easy to understand what they had to do to complete the assessment. Scoring accuracy was 88% at the word item level, with reprocessing to address accent, context setting, and other NLP approaches to improve accuracy underway e.g., homophones biased to the grocery item.ConclusionWhilst acceptability was high, the importance of localization of test stimuli was confirmed as well as the need for additional data processing options to account for accent. Continued optimization of data processing to score participant responses and word item pools to reduce ambiguity is expected to further improve accuracy e.g., accounting for regional accents and consideration of homophones and compound words. However, specific characteristics of speech including in those for whom English may be a second/foreign language will be an area of focus for future development.

Further Validation of a Self‐administered Smartphone‐based Verbal Learning and Memory Assessment

AbstractBackgroundThe International Shopping List test (ISLT) is a verbal word list learning test set within a realistic shopping list context, supporting ease of cultural adaptation, and addressing content validity and patient relevance. Improvements to and the wide adoption of natural language processing (NLP) software has enabled the possibility for self‐administered tests of verbal learning. LilaTM a self‐administered, smartphone‐application (app) was developed, using the virtual assistant and NLP. We have previously reported acceptability and usability data from qualitative analysis of concept elicitation interviews. We now seek to provide preliminary validation data around its use.MethodA convenience sample of healthy young adults (n = 40), M(SD)age = 35 (16), were invited to install and complete one assessment of the LilaTM app. A usability survey was also administered to explore participants’ experiences. The sample was predominantly female (70%) and of Asian ethnicity (62.5%). Data were collected in Australia, using an English word pool developed for the USA.ResultAcceptability was generally high with N = 38 completing immediate recall rounds and N = 32 completing the delayed recall round. Eight‐two percent found it extremely or somewhat easy to hear and understand what items were on the shopping list and 95% indicated that it was extremely or somewhat easy to understand what they had to do to complete the assessment. Scoring accuracy was 88% at the word item level, with reprocessing to address accent, context setting, and other NLP approaches to improve accuracy underway e.g., homophones biased to the grocery item.ConclusionWhilst acceptability was high, the importance of localization of test stimuli was confirmed as well as the need for additional data processing options to account for accent. Continued optimization of data processing to score participant responses and word item pools to reduce ambiguity is expected to further improve accuracy e.g., accounting for regional accents and consideration of homophones and compound words. However, specific characteristics of speech including in those for whom English may be a second/foreign language will be an area of focus for future development.

Implementation Of Natural Language Processing for Spam Email Detection in Outcome Based Education (OBE) Application

The Natural Language Processing (NLP) approach has been proven to be effective in spam detection in e-mail because of its ability to process text and identify patterns and distinctive characteristics of spam e-mail. Methods in this NLP approach include data pre-processing, such as removing punctuation, irrelevant common words, tokenization, stemming, and others, as well as classification techniques such as Support Vector Classifier (SVC), Naive Bayes, and others. In testing various models, there is one model that shows the highest precision with the number 0.98. This study shows that the NLP approach provides better performance in spam detection compared to other methods. However, it is necessary to improve technology and develop more complex detection methods to improve the performance and accuracy of the email spam detection model

Incivility in COVID-19 Vaccine Mandate Discourse and Moral Foundations: Natural Language Processing Approach.

Vaccine hesitancy poses a substantial threat to efforts to mitigate the harmful effects of the COVID-19 pandemic. To combat vaccine hesitancy, officials in the United States issued vaccine mandates, which were met with strong antivaccine discourse on social media platforms such as Reddit. The politicized and polarized nature of COVID-19 on social media has fueled uncivil discourse related to vaccine mandates, which is known to decrease confidence in COVID-19 vaccines. This study examines the moral foundations underlying uncivil COVID-19 vaccine discourse. Moral foundations theory poses that individuals make decisions to express approval or disapproval (ie, uncivil discourse) based on innate moral values. We examine whether moral foundations are associated with dimensions of incivility. Further, we explore whether there are any differences in the presence of incivility between the r/coronaviruscirclejerk and r/lockdownskepticism subreddits. Natural language processing methodologies were leveraged to analyze the moral foundations underlying uncivil discourse in 2 prominent antivaccine subreddits, r/coronaviruscirclejerk and r/lockdownskepticism. All posts and comments from both of the subreddits were collected since their inception in March 2022. This was followed by filtering the data set for key terms associated with the COVID-19 vaccine (eg, "vaccinate" and "Pfizer") and mandates (eg, "forced" and "mandating"). These key terms were selected based on a review of existing literature and because of their salience in both of the subreddits. A 10% sample of the filtered key terms was used for the final analysis. Findings suggested that moral foundations play a role in the psychological processes underlying uncivil vaccine mandate discourse. Specifically, we found substantial associations between all moral foundations (ie, care and harm, fairness and cheating, loyalty and betrayal, authority and subversion, and sanctity and degradation) and dimensions of incivility (ie, toxicity, insults, profanity, threat, and identity attack) except for the authority foundation. We also found statistically significant differences between r/coronaviruscirclejerk and r/lockdownskepticism for the presence of the dimensions of incivility. Specifically, the mean of identity attack, insult, toxicity, profanity, and threat in the r/lockdownskepticism subreddit was significantly lower than that in the r/coronaviruscirclejerk subreddit (P<.001). This study shows that moral foundations may play a substantial role in the presence of incivility in vaccine discourse. On the basis of the findings of the study, public health practitioners should tailor messaging by addressing the moral values underlying the concerns people may have about vaccines, which could manifest as uncivil discourse. Another way to tailor public health messaging could be to direct it to parts of social media platforms with increased uncivil discourse. By integrating moral foundations, public health messaging may increase compliance and promote civil discourse surrounding COVID-19.