NLP Techniques Research Articles

NLP-Powered Resume Matching For Recruitment

More recently, recruitment has largely relied on the automation of the matching process between candidates and job roles. In this regard, this paper focuses on the development of a resume parser application utilizing NLP techniques, PDF text extraction, and machine learning-based evaluation of resumes according to job descriptions. In developing the application using the Flask framework, users are allowed to upload resume and job description files in PDF format. The system automatically extracts the text, preprocesses it, and performs the task of skill matching. It also computes a semantic similarity score based on term frequency-inverse document frequency and cosine similarity techniques. Using a trained machine learning model, the application predicts a binary job fit score based on its semantic similarity and skills matching metrics scores. This paper outlines the design, implementation, and evaluation of the system, and it indeed has the potential to assist the recruiters in pre-screening the candidates.

A Natural Language Processing Approach for Sentiment Analysis of Hotel Reviews

Businesses rely heavily on review analysis of consumer feedback and reviews when making decisions. This research examines fifteen years' worth of hotel evaluations to learn more about the opinions of visitors and how they relate to review scores in various years and regions. After the dataset was cleansed to concentrate on reviews written in English, TextBlob was used to analyze sentiment and classify the reviews as positive, neutral, or negative. The results validate using sentiment analysis in conjunction with numerical ratings, which show a positive link between more excellent ratings and positive sentiment scores. A significant drop in sentiment scores following the 2008–2009 recession emphasizes how the global financial crisis affected the hospitality sector. Significant differences in guest feelings between cities, states, and nations are shown by geographic analysis, underscoring the necessity of region-specific approaches. Sentiment distribution and trends were revealed via intricate visualizations such as heat maps, box plots, scatter plots, and histograms. To improve guest happiness, the study uses an understanding of geographical variations. These results give hotel management a solid framework for raising customer satisfaction and loyalty, customizing marketing campaigns, and enhancing service quality. For deeper insights, future studies may investigate more sophisticated NLP techniques.

Integrating Twitter Sentiment with ML Models for Enhanced Stock Forecasting

This research investigates the integration of Twitter sentiment analysis with machine learning models, specifically Long Short-Term Memory (LSTM), Facebook Prophet, and Neural Prophet, to enhance the accuracy of stock market predictions. While these models are accurate in capturing long-term trends and seasonality, they often fail when exploring the short-term volatility in stock prices. By using real-time social media sentiments, we aim to improve short-term prediction accuracy. Sentiment data from Twitter, extracted and classified using NLP techniques, was integrated with the models to predict stock prices for Apple Inc. (AAPL), Paramount Global (PARA), and BP plc (BP) over the period 2016-2024. The study compares the performance of the models using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that Neural Prophet stood the best in terms of its accuracy and efficiency in stock predictions when compared to LSTM and Facebook Prophet, reducing RMSE by 25% and MAE by 20%. While the integration of sentiment analysis improved prediction accuracy, limitations remain in predicting the scale of sudden market changes. The findings highlight the potential of sentiment analysis in stock prediction but also call for further model enhancements to fully address short-term market volatility.

Enhanced UrduAspectNet: Leveraging Biaffine Attention for superior Aspect-Based Sentiment Analysis

Urdu, with its rich linguistic complexity, poses significant challenges for computational sentiment analysis. This study presents an enhanced version of UrduAspectNet, specifically designed for Aspect-Based Sentiment Analysis (ABSA) in Urdu. We introduce key innovations including the incorporation of Biaffine Attention into the model architecture, which synergizes XLM-R embeddings, a bidirectional LSTM (BiLSTM), and dual Graph Convolutional Networks (GCNs). Additionally, we utilize dependency parsing to create the adjacency matrix for the GCNs, capturing syntactic dependencies to enhance relational representation. The improved model, termed Enhanced UrduAspectNet, integrates POS and lemma embeddings, processed through BiLSTM and GCN layers, with Biaffine Attention enhancing the extraction of intricate aspect and sentiment relationships. We also introduce the use of BIO tags for aspect term identification, improving the granularity of aspect extraction. Experimental results demonstrate significant improvements in both aspect extraction and sentiment classification accuracy. This research advances Urdu sentiment analysis and sets a precedent for leveraging sophisticated NLP techniques in underrepresented languages.

Geoinference of author affiliations using NLP-based text classification

Author affiliations are essential in bibliometric studies, requiring relevant information extraction from free-text affiliations. Precisely determining an author’s location from their affiliation is crucial for understanding research networks, collaborations, and geographic distribution. Existing geoparsing tools using regular expressions have limitations due to unstructured and ambiguous affiliations, resulting in erroneous location identification, especially for unconventional variations or misspellings. Moreover, their inefficient handling of big datasets hampers large-scale bibliometric studies. Though machine learning-based geoparsers exist, they depend on explicit location information, creating challenges when detailed geographic data is absent. To address these issues, we developed and evaluated a natural language processing model to predict the city, state, and country from an author’s free-text affiliation. Our model automates location inference, overcoming drawbacks of existing methods. Trained and tested with MapAffil, a publicly available geoparsed dataset of PubMed affiliations up to 2018, our model accurately retrieves high-resolution locations, even without explicit mentions of a city, state, or even country. Leveraging NLP techniques and the LinearSVC algorithm, our machine learning model achieves superior accuracy based on several validation datasets. This research demonstrates a practical application of text classification for inferring specific geographical locations from free-text affiliations, benefiting researchers and institutions in analyzing research output distribution.

Implementation of a Chatbot System (College Enquiry)

The rapid advancement of artificial intelligence has led to the development of more sophisticated chatbots capable of enhancing user interactions. This project presents a chatbot system that integrates speech recognition with traditional text input, enabling users to interact through both voice and text. Using NLP techniques such as tokenization, lemmatization, and part-of-speech tagging, the system effectively processes user inputs to match predefined intents stored in a JSON file. By integrating Google’s Speech-to-Text API, this chatbot dynamically processes voice commands, offering an improved, flexible user experience. The system is easily adaptable to various domains through the update of intents, providing a robust solution for dynamic, real-time conversational agents.

A Proposed Ensemble Approach for Searching Hacking News Semantically

Efficient search of hacking information has been a topic of great discussion in recent years. Many challenges are encountered when searching for this information. In particular, researchers may encounter unfamiliar and potentially challenging terms, ideas, tools, and other items that are unique to hacking. Effective comprehension of synonyms and polysemy is necessary. These reasons serve as the driving force behind our efforts to develop a productive method for semantic hacking information searches. Semantic search, using advanced NLP techniques, has transformed information retrieval by improving search result accuracy and relevance. Unlike traditional lexical methods, neural models like sentence-transformers handle synonyms and polysemy efficiently. However, processing time increases with model size. This paper proposes a novel ensemble semantic search (NESS) approach that aggregates mini or small neural embedding models, leveraging their distinct advantages. Evaluated on a dataset with over 300,000 Hacker News stories, our proposed method significantly enhances ranking quality and retrieval accuracy compared to existing techniques, while requiring half the processing time of the best-performing large model. The findings underscore the trade-offs between model complexity, retrieval accuracy, and processing efficiency, offering insights for optimizing semantic search systems.

Institutional Theory and Flight Schools: An Analysis of Conformity in the Skies

This study employs a comprehensive bibliometric and content analysis to explore the application of institutional theory within the context of flight schools and aviation education. Utilizing data extracted from the Web of Science (WoS) database, this research examines the most influential studies, authors, and research trends in this field from 2000 to 2023. The findings highlight the critical role of institutional pressures and logics in shaping the operations and evolution of flight schools. This study contributes to the existing literature by integrating institutional theory with bibliometric methods, offering a comprehensive understanding of the factors influencing aviation education and identifying areas for future research. Limitations include the dataset's restriction to publications indexed in the WoS and the basic NLP tools used for analysis. Future research should incorporate more advanced NLP techniques, consider additional databases, and explore the impact of emerging technologies on aviation education.

Transformer-Based Video Comment Analysis

In this work, we conduct a comprehensive analysis of sentiment in Bilibili comments using a Transformer-based model. We employ the mT5_m2o_chinese_simplified_crossSum model, a multitasking Transformer model specifically designed for summarizing Chinese text. The study involves scraping comments from a video related to the "trolley problem," followed by data cleaning and preprocessing. The preprocessed data is fed into the Transformer model, which generates summaries that accurately reflect the main viewpoints and discussions in the comments. Our results show that the model effectively captures key ethical dilemmas, personal opinions, and emotional responses, providing a nuanced understanding of public sentiment. This research highlights the potential of advanced NLP techniques in processing large volumes of user-generated content, offering valuable insights for businesses and researchers in understanding public opinion and facilitating ethical discussions. The study underscores the importance of optimizing Transformer models for specific tasks, demonstrating their flexibility and performance in text summarization. These findings provide a robust foundation for future applications and innovations in natural language processing.

A Study on Improving the Accuracy and Effectiveness of Similarity Detection Processes in Text Files Using NLP Techniques

A Study on Improving the Accuracy and Effectiveness of Similarity Detection Processes in Text Files Using NLP Techniques

Digital Democracy: Analyzing Political Sentiments through Multinomial Naive Bayes in Election Campaign Ads

This research delves into sentiment analysis for digital election campaign advertisements using the Multinomial Naive Bayes approach. The study addresses the limitations of standard sentiment analysis methodologies in capturing the intricacies of public sentiments toward political ads. The dataset, sourced from Kaggle, encompasses 3000 records with sentiments categorized as positive, neutral, and negative. The Multinomial Naive Bayes model demonstrated a substantial accuracy increase from 92% to 96%, outperforming the standard Naive Bayes model. Precision, recall, and F1-score metrics consistently improved across sentiment categories. While dataset representativeness and cultural specificity pose limitations, the research contributes significantly to sentiment analysis methodologies in politically charged digital environments. Future research recommendations include exploring advanced NLP techniques, incorporating real-time data from diverse social media platforms, and addressing ethical considerations in political sentiment analysis. The outcomes emphasize the importance of tailored methodologies for enhanced accuracy in understanding sentiments expressed in digital election campaign advertisements.

MAPRS: An intelligent approach for post-prescription review based on multi-label learning

Antimicrobial resistance (AMR) is a major threat to public health worldwide. It is a promising way to improve appropriate prescription by the review and stewardship of antimicrobials, and Post-Prescription Review (PPR) is currently the main tool used in hospitals. Existing methods of PPR typically focus on the dichotomy of antimicrobial prescription based on binary classification which, however, is usually a multi-label classification problem. Moreover, previous research did not explain the causes beneath the inappropriate antimicrobial used in the clinical setting, which could be practically important for problem location and decision improvement. In this paper, we collected antimicrobial prescriptions and related data from clean surgery in a hospital in northeastern China, and proposed a Multi-label Antimicrobial Post-Prescription Review System (MAPRS). MAPRS first uses NLP techniques to process unstructured data in prescriptions and explores the value of clinical record text for solving medical problems. Then, Classifier Chains are used to deal with multi-label problems and fused with machine learning algorithms to construct a classifier. At last, a SHAP explanation module is introduced to explain the inappropriate prescriptions. The experimental results show that MAPRS could achieve great performance in a challenging six-category multi-label task, with a subset accuracy of 90.7 % and an average AUROC of 94.3 %. Our results can help hospitals to perform intelligent prescription review and improve the antimicrobial stewardship.

Leveraging large language models through natural language processing to provide interpretable machine learning predictions of mental deterioration in real time

AbstractBased on official estimates, 50 million people worldwide are affected by dementia, and this number increases by 10 million new patients every year. Without a cure, clinical prognostication and early intervention represent the most effective ways to delay its progression. To this end, artificial intelligence and computational linguistics can be exploited for natural language analysis, personalized assessment, monitoring, and treatment. However, traditional approaches need more semantic knowledge management and explicability capabilities. Moreover, using large language models (llms) for cognitive decline diagnosis is still scarce, even though these models represent the most advanced way for clinical–patient communication using intelligent systems. Consequently, we leverage an llm using the latest natural language processing (nlp) techniques in a chatbot solution to provide interpretable machine learning prediction of cognitive decline in real-time. Linguistic-conceptual features are exploited for appropriate natural language analysis. Through explainability, we aim to fight potential biases of the models and improve their potential to help clinical workers in their diagnosis decisions. More in detail, the proposed pipeline is composed of (i) data extraction employing nlp-based prompt engineering; (ii) stream-based data processing including feature engineering, analysis, and selection; (iii) real-time classification; and (iv) the explainability dashboard to provide visual and natural language descriptions of the prediction outcome. Classification results exceed 80% in all evaluation metrics, with a recall value for the mental deterioration class about 85%. To sum up, we contribute with an affordable, flexible, non-invasive, personalized diagnostic system to this work.

From Data to Insight: Transforming Online Job Postings into Labor-Market Intelligence

In the continuously changing labor market, understanding the dynamics of online job postings is crucial for economic and workforce development. With the increasing reliance on Online Job Portals, analyzing online job postings has become an essential tool for capturing real-time labor-market trends. This paper presents a comprehensive methodology for processing online job postings to generate labor-market intelligence. The proposed methodology encompasses data source selection, data extraction, cleansing, normalization, and deduplication procedures. The final step involves information extraction based on employer industry, occupation, workplace, skills, and required experience. We address the key challenges that emerge at each step and discuss how they can be resolved. Our methodology is applied to two use cases: the first focuses on the analysis of the Greek labor market in the tourism industry during the COVID-19 pandemic, revealing shifts in job demands, skill requirements, and employment types. In the second use case, a data-driven ontology is employed to extract skills from job postings using machine learning. The findings highlight that the proposed methodology, utilizing NLP and machine-learning techniques instead of LLMs, can be applied to different labor market-analysis use cases and offer valuable insights for businesses, job seekers, and policymakers.

Deciphering Arabic question: a dedicated survey on Arabic question analysis methods, challenges, limitations and future pathways

This survey reviews different research on question analysis, including other comparative studies of question analysis approaches and an evaluation of the questions by different NLP techniques that are used in question interpretation and categorization. Among these key findings noted includes the assessment of deep learning models such as M-BiGRU-CNN and M-TF-IDF, which come with high precision and accuracy when applied with the effectiveness of use in dealing with the complexities involved in a language. Some of the most mature machine learning algorithms, for example, SVM or logistic regression, remain powerful models, especially on the classification task, meaning that the latter continues to be relevant. This study further underlines the applicability of rule-based or hybrid methodologies in certain linguistic situations, and it must be said that custom design solutions are required. We could recommend, on this basis, directing future work towards the integration of these hybrid systems and towards the definition of more general methodologies of evaluation that are in line with the constant evolution of NLP technologies. It revealed that the underlying challenges and barriers in the domain are very complex syntactic and dialectic variations, unavailability of software tools, very critical standardization in Arabic datasets, benchmark creation, handling of translated data, and the integration of Large Language Models (LLMs). The paper discusses the lack of identity and processing of such structures through online systems for comparison. This comprehensive review highlights not only the diversified potential for the capabilities of NLP techniques in refining question analysis but also the potential way of great promises for further enhancements and improvements in this progressive domain.

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

Am I Hurt?: Evaluating Psychological Pain Detection in Hindi Text Using Transformer-based Models

The automated evaluation of pain is critical for developing effective pain management approaches that seek to alleviate pain while preserving patients’ functioning. Transformer-based models can aid in detecting pain from Hindi text data gathered from social media by leveraging their ability to capture complex language patterns and contextual information. By understanding the nuances and context of Hindi text, transformer models can effectively identify linguistic cues and sentiments and expressions associated with pain, enabling the detection and analysis of pain-related content present in social media posts. The purpose of this research is to analyze the feasibility of utilizing NLP techniques to automatically identify pain within Hindi textual data, providing a valuable tool for pain assessment in Hindi-speaking populations. The research showcases the HindiPainNet model, a deep neural network that employs the IndicBERT model, classifying the dataset into two class labels {pain, no_pain} for detecting pain in Hindi textual data. The model is trained and tested using a novel dataset, दर्द-ए-शायरी (pronounced as Dard-e-Shayari ), curated using posts from social media platforms. The results demonstrate the model's effectiveness, achieving an accuracy of 70.5%. This pioneer research highlights the potential of utilizing textual data from diverse sources to identify and understand pain experiences based on psychosocial factors. This research could pave the path for the development of automated pain assessment tools that help medical professionals comprehend and treat pain in Hindi-speaking populations. Additionally, it opens avenues to conduct further NLP-based multilingual pain detection research, addressing the needs of diverse language communities.

Improving fake job description detection using deep learning-based NLP techniques

ABSTRACT In the modern online society, where social networks are increasingly developed, the authenticity of information is one of the essential needs. There are many problems with detecting fake information, such as fake news detection, fake review detection, etc. Fake job description (FJD) detection is an interesting problem many groups have studied recently. However, current studies still need improvement in predictability. Therefore, this study develops a new method named NLP2FJD that utilizes deep learning-based NLP techniques for improving FJD detection. Firstly, we utilize the pre-trained Word2Vec to extract features from textual information from the dataset. Next, combining textual information and meta-information in the experimental dataset to improve the performance of FJD detection. The above two improvements will help the recommender system significantly improve the predictive ability of the proposed model. Finally, the empirical experiments are conducted to confirm the effectiveness of the proposed method on the experimental dataset compared with cutting-edge methods. The experimental results demonstrate that the NLP2FJD framework transcends other experimental methods for FJD detection on the experimental dataset. Besides, this study also conducts ROC curve analysis to show how to determine the optimal threshold for distinguishing the fake or real job description on the experimental dataset.

QuoteInspector: Gaining Insight about Social Media Discussions

Our greatest source of insight into the real world today is via social media. Here, a major statement or quote by a public figure (world leader, politician, celebrity, scientist) can have wide-ranging impact, igniting extensive discussions and triggering reactions. It would be helpful to have tools for monitoring, querying, and inspecting the "flow" of social discourse. We introduce QuoteInspector, a system uniquely designed for efficient tracking and analysis of social media discussions around quotes. QuoteInspector leverages modern text embeddings and employs a clustering-based methodology for extracting topics from posts; it further integrates various NLP techniques for in-depth cluster analysis. Additionally, the system enhances the user experience by combining keyword- and relationship-based (structured) search for efficient and precise quote retrieval.

Revolutionizing AI Driven Innovations in Gemstone Classification: A Synergistic Approach Integrating Visual and Semantic NLP Techniques

Revolutionizing AI Driven Innovations in Gemstone Classification: A Synergistic Approach Integrating Visual and Semantic NLP Techniques