Natural Language Processing Techniques Research Articles

Unveiling Text Representation with 'Bag of Words'

Techniques for natural language processing (NLP) have grown to be essential tools for deciphering and drawing insightful conclusions from massive volumes of text data. A thorough review of numerous natural language processing (NLP) techniques, including as tokenization, stemming, lemmatization, named entity recognition, sentiment analysis, and topic modelling, is provided in this abstract. These methods are essential for applications like sentiment analysis, machine translation, text assistant categorization, and information retrieval. Furthermore, the capabilities of NLP systems have been greatly improved by recent developments in deep learning, especially with models like BERT and GPT. This has allowed them to reach state-of-the-art performance in a variety of language understanding tasks. The difficulties and potential paths for future study in NLP, including managing ambiguity, comprehending context, and enhancing multilingual assistance, are also highlighted in this abstract. Using NLP tools to their full potential, researchers.

Understanding the social construction of juvenile delinquency: insights from semantic analysis of big-data historical newspaper collections

AbstractMassive historical newspaper collections contain rich information about the historical development of social issues and constitute a unique resource for studying the social construction of issues such as juvenile delinquency. However, manual analysis of millions of pages of newspaper articles is infeasible. In this paper, we propose a suite of computational methods, including cross-context lexical analysis, dynamic semantic analysis, and valence analysis, to facilitate the study of historical social construction. We apply these methods to ProQuest Historical Newspapers$$^{\textrm{TM}}$$ TM collection in the period of 1790–2006 to study the social construction of juvenile delinquency over this period. Our results show that the proposed methods are effective in revealing insights regarding the social construction of juvenile delinquency, leading to a better understanding of this complex issue and specific hypotheses for further study. Overall, our study shows the great promise of leveraging natural language processing techniques for analyzing historical news data to study social construction of societal issues.

Less is more? Communicating SDG orientation and enterprises' economic performance

As the interest in sustainable development increases, businesses can benefit from aligning their orientation with the Sustainable Development Goals (SDGs). It remains unclear, however, how focusing on a broader or narrower set of SDGs affects enterprises' economic performance. This study examines the impact of a communicated SDG orientation on the economic performance of social enterprises and traditional commercial businesses. Using natural language processing (NLP) techniques to analyse textual content from 661 enterprises' websites, we found a positive relationship between the communication of a narrow set of SDGs and enterprises' economic performance. The extent of this effect is similar between social and traditional commercial enterprises. Therefore, stakeholders may value an enterprise's SDG orientation strategy that focuses on a narrow set of SDGs in distinct purpose-driven institutional contexts.

Hype or hope? Ketamine for the treatment of depression: results from the application of deep learning to Twitter posts from 2010 to 2023

ObjectiveTo investigate societal perceptions of ketamine’s use in depression therapy by analysing Twitter posts from January 1, 2010 to April 1, 2023.MethodsUsing Twitter as the social media platform of choice, and employing search terms based on (depression OR depressed OR depressive) AND (ketamine OR esketamine OR Spravato), we collected English-language tweets from January 1, 2010, to April 1, 2023. Using unsupervised machine learning and natural language processing (NLP) techniques, including Bidirectional Encoder Representations from Transformers (BERT) and BERTopic, the study identified prevalent topics surrounding public chatter around the use of ketamine in depression treatment. Manual thematic analyses further refined these topics into themes.ResultsOut of an initial dataset of 99,405 tweets, after removing duplicate tweets, re-tweets and tweets posted by organizations over Twitter, 18,899 unique tweets from presumably individual users were analysed. Analysis of temporal trends revealed a shift in public attitudes, particularly after the United States Food and Drug Administration (FDA)’s 2019 approval of ketamine for depression. Three major themes emerged: a changing regulatory landscape, cautious optimism, and personal experiences with the drug. There was an initial spike in discussions post-FDA approval in 2019. Thereafter, cautious optimism (Theme 2) decreased among the general public, with more personal accounts (Theme 3) highlighting the potential benefits for some treatment-resistant patients. Limitations of the study include Twitter’s inherent biases towards younger, English-speaking demographics.ConclusionIn summary, the public’s multifaceted perception leans towards a hopeful stance on ketamine’s therapeutic potential for depression.

Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.

To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging. A training and testing dataset of reports of 979 CT or MRI scans of the brain for patients admitted to the neurosurgery service of a single hospital in June 2021 or to the Emergency Department between July 1-8, 2021, was identified. A variety of machine learning and deep learning algorithms utilizing natural language processing were trained on the training set (84% of the total cohort) and tested on the remaining images. A subset comparison cohort (n = 76) was then assessed to compare output of the best algorithm against real-life inpatient documentation. For "brain compression", a random forest classifier outperformed other candidate algorithms with an accuracy of 0.81 and area under the curve of 0.90 in the testing dataset. For "brain edema", a random forest classifier again outperformed other candidate algorithms with an accuracy of 0.92 and AUC of 0.94 in the testing dataset. In the provider comparison dataset, for "brain compression," the random forest algorithm demonstrated better accuracy (0.76 vs 0.70) and sensitivity (0.73 vs 0.43) than provider documentation. For "brain edema," the algorithm again demonstrated better accuracy (0.92 vs 0.84) and AUC (0.45 vs 0.09) than provider documentation. A natural language processing-based machine learning algorithm can reliably and reproducibly identify selected common neurosurgical comorbidities from radiology reports. This result may justify the use of machine learning-based decision support to augment provider documentation.

Democratizing Data Visualization and Insights Extraction with Pandas, Generative AI, and CSV Data

Data visualization and insights extraction are crucial components of modern data-driven decision-making processes. However, traditional methods often require extensive coding knowledge, creating barriers for non-technical users. This whitepaper presents a comprehensive solution that integrates the powerful data manipulation capabilities of the Pandas library with cutting-edge Generative AI and natural language processing techniques. By leveraging a fine-tuned GPT-3 model trained on a diverse corpus of data analysis and visualization resources, our approach enables users to upload CSV data files and receive automated insights, default visualizations, and the ability to generate custom visualizations through intuitive natural language prompts. The solution streamlines the workflow, eliminating the need for coding expertise while ensuring data privacy and integrity within a secure execution environment. User studies and benchmarking demonstrate increased productivity, time savings, and high user satisfaction. This solution has the potential to democratize data analysis and visualization, empowering decision-makers across various industries with data-driven insights and informed decision-making processes.

Identifying specific details from text to populate databases and generate summaries using Named Entity Recognition

Named Entity Recognition (NER) is a natural language processing (NLP) technique that focuses on identifying and classifying entities within a text. Entities are typically real-world objects that have names, such as people, organizations, locations, dates, percentages, currencies, and more. NER is used to extract structured information from unstructured text data and is an important component of various NLP applications, including question generation, information retrieval, text summarization, and more. Index Terms— Named Entity Recognition (NER), Natural language processing (NLP), text summarization.

Automating literature screening and curation with applications to computational neuroscience.

ModelDB (https://modeldb.science) is a discovery platform for computational neuroscience, containing over 1850 published model codes with standardized metadata. These codes were mainly supplied from unsolicited model author submissions, but this approach is inherently limited. For example, we estimate we have captured only around one-third of NEURON models, the most common type of models in ModelDB. To more completely characterize the state of computational neuroscience modeling work, we aim to identify works containing results derived from computational neuroscience approaches and their standardized associated metadata (eg, cell types, research topics). Known computational neuroscience work from ModelDB and identified neuroscience work queried from PubMed were included in our study. After pre-screening with SPECTER2 (a free document embedding method), GPT-3.5, and GPT-4 were used to identify likely computational neuroscience work and relevant metadata. SPECTER2, GPT-4, and GPT-3.5 demonstrated varied but high abilities in identification of computational neuroscience work. GPT-4 achieved 96.9% accuracy and GPT-3.5 improved from 54.2% to 85.5% through instruction-tuning and Chain of Thought. GPT-4 also showed high potential in identifying relevant metadata annotations. Accuracy in identification and extraction might further be improved by dealing with ambiguity of what are computational elements, including more information from papers (eg, Methods section), improving prompts, etc. Natural language processing and large language model techniques can be added to ModelDB to facilitate further model discovery, and will contribute to a more standardized and comprehensive framework for establishing domain-specific resources.

Data-driven automatic classification model for construction accident cases using natural language processing with hyperparameter tuning

The construction industry, while vital to societal progress, is marred by a high incidence of accidents and injuries. Manual classification of accident cases is intensive and susceptible to human bias. This study addresses this challenge by developing an automated accident case classification system for the construction industry using Natural Language Processing and machine learning techniques. This study was conducted using the following steps: (1) Establishment of dataset (2) Korean Natural Language Processing (3) Selection of machine learning models (4) Model evaluation. The models exhibited competitive performance, demonstrating high accuracy, precision, and recall rates across all classification tasks. XGBoost outperformed NB, SVM, and KNN for accident type, facility type, and work type with accuracy of 0.80, 0.56, and 0.67, respectively. The results also provided insights into the factors influencing accident classification. This study contributes to construction safety by providing a data-driven foundation for safety decision-making, resource allocation, and benchmarking.

Machine Learning Based Spam Comments Detection on Youtube: A Comparison of Regression Models

The rise of spam comments on platforms like YouTube has become a significant concern, as they not only hinder genuine user engagement but also pose serious risks to users' safety and privacy. Machine Learning (ML) offers a powerful solution to combat spam comments by automating the process of detecting and preventing them. With the ability to analyze vast amounts of data and patterns, ML algorithms can effectively distinguish between legitimate comments and those that are spam. One of the commonly employed approaches in ML for spam comment detection is the Naive Bayes classification algorithm. Naive Bayes is a probabilistic algorithm that calculates the likelihood of a comment being spam based on its characteristics and the occurrence of specific keywords or phrases that are typical of spam content. By training the algorithm on a labeled dataset of spam and non-spam comments, it can learn to recognize patterns and generalize its understanding to new, unseen comments. Achieving a detection accuracy of 92.78% is indeed promising, but researchers and developers continue to explore other ML techniques and combinations to further improve the accuracy and robustness of spam comment detection systems. Ensemble methods, deep learning, and Natural Language Processing (NLP) techniques are among the advanced approaches gaining attention in this domain. One crucial aspect of an effective spam detection system is its adaptability and responsiveness to emerging spam tactics.

The Double-Edged Sword of Exemplar Similarity

We investigate how a firm’s positioning relative to category exemplars shapes security analysts’ evaluations. Using a two-stage model of evaluation (initial screening and subsequent assessment), we propose that exemplar similarity enhances a firm’s recognizability and legitimacy, increasing the likelihood that it passes the initial screening stage and attracts analyst coverage. However, exemplar similarity may also prompt unfavorable comparisons with exemplar firms, leading to lower analyst recommendations in the assessment stage. We further argue that category coherence, distinctiveness, and exemplar typicality influence the impact of exemplar similarity on firm evaluation. Leveraging natural language processing (NLP) techniques to analyze a sample of 7,603 U.S. public firms from 1997 to 2022, we find robust support for our predictions. By highlighting the intricate role of strategic positioning vis-à-vis category exemplars in shaping audience evaluations, our findings have important implications for research on positioning relative to category exemplars, category viability, optimal distinctiveness, and security analysts. Supplemental Material: The online appendices are available at https://doi.org/10.1287/orsc.2022.16855 .

Computer Feed based Text Processing Using NLP

The goal of this research project is to create a comprehensive multimedia and text processing framework that can be used in a variety of applications. The objective is to combine a collection of libraries and instruments for the purpose of extracting, analyzing, and modifying textual information obtained from multimedia files, including images, videos, and audio files. The strategy involves applying cutting-edge techniques such as computer vision, audio processing, natural language processing (NLP), optical character recognition (OCR), and computer vision. Important findings include accurate text extraction from multimedia inputs, accurate textual data analysis using natural language processing techniques, and the production of insightful information via sentiment analysis, summarization, and translation. Its main contributions are related to the development of a flexible system that can process a wide range of multimedia inputs and provide useful text processing features. This project provides a foundation for future research and useful applications in the fields of text processing and multimedia.

Deteksi Cyberbullying Menggunakan BERT dan Bi-LSTM

Cyberbullying is a digital problem that is not a new phenomenon. This existed before the advent of social networks, and cyberbullying has a wide impact, including a person's mental and physiological conditions such as sadness, anxiety and depression. The main objective of this research is to develop an effective cyberbullying detection system using natural language processing techniques. The method used in this research includes the application of the BERT (Bi-Directional Encoder Representations from Transformers) and Bi-LSTM (Bi-Directional Long Short-Term Memory) models as a deep learning approach to analyze text and detect cyberbullying behavior. This approach allows the system to understand complex language contexts and capture patterns that traditional methods may find difficult to identify. Testing was carried out using a dataset that included various types of Indonesian language texts containing cyber bullying acts. The research results show that the combination of BERT and Bi-LSTM is able to provide superior detection performance with a high accuracy rate of 90% and the ability to identify variations of cyber bullying. This research makes a significant contribution to efforts to protect individuals from the negative impacts of cyber bullying through the development of a sophisticated and adaptive detection system.

SURVEY ON STABLE DIFFUSION TEXT TO IMAGE USING AI

The Fusion Nexus Text-to-Image Synthesis Initiative integrates cutting-edge Generative Adversarial Networks (GANs) with Natural Language Processing (NLP) techniques to narrow the semantic divide between textual input and visual output. Built upon the robust Stable Diffusion training paradigm, this initiative is engineered to produce immersive, true-to-life images based on descriptive text prompts. While GANs have exhibited potential in image generation, issues like mode collapse and training instability have impeded their effectiveness. The Fusion Nexus initiative circumvents these challenges by leveraging the Stable Diffusion framework, which furnishes a stable and reliable training methodology for GANs. By amalgamating recent advancements in deep learning, this project spearheads a novel approach to text-to-image synthesis. Its primary aim is to craft cohesive and highly realistic visual representations from textual descriptions, thereby bridging the gap between linguistic expression and visual perception. This ambitious undertaking marks a significant stride at the convergence of GANs and NLP, presenting a promising solution to the intricate task of text-to-image generation. Keywords: Adversarial, Diffusion, Framework, Fusion, GANs, Generation, Generation, Image, Language, Natural, NLP, Processing, Robust, Stable, Text-to-image, Training, Visuals.

Sentiment-driven Movie Recommendation System: A Machine Learning Approach

Pioneering method for enhancing movie recommendation systems through the integration of sentiment analysis of micro-blogging data. Leveraging natural language processing (NLP) techniques, the proposed system extracts sentiment from tweets and other micro-blogging sources pertinent to movies systematic approach encompassing the collection, preprocessing, and analysis of movie review data tailored for sentiment analysis tasks. Key aspects covered include the acquisition of publicly available datasets, methodologies for web scraping, preprocessing techniques, strategies for sentiment labeling, methods for data augmentation, procedures for data splitting, optimal data storage formats, and ethical considerations inherent in data collection and utilization. By offering a comprehensive guide, furnish both researchers and practitioners with the necessary tools to proficiently manage movie review data while navigating the ethical intricacies associated with its acquisition and application.

Integrating NLP Chatbot into Career Guidance Web Application

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that typically require human intelligence. The development of an advanced web-based chatbot system involves the integration of various technologies and methodologies to create a seamless conversational interface. Leveraging HTML, CSS, and JavaScript for frontend development, alongside Python Flask framework for backend functionality, the project aims to deliver a robust and intuitive user experience. Key to the system's operation is the incorporation of PyTorch for machine learning, allowing for the implementation of sophisticated natural language processing (NLP) techniques. Through tokenization and stemming using NLTK (Natural Language Toolkit), the system enhances its understanding of user queries, enabling it to provide relevant and contextually appropriate responses. At the core of the system lies a supervised learning model trained on a custom dataset compiled from user interactions. This model utilizes bag-of-words representations and neural network architectures to predict responses based on input text. By continuously refining its predictions through feedback mechanisms, the system improves its accuracy and responsiveness over time, ensuring a more tailored and effective user experience. Voice input functionality, facilitated by speech recognition technology, further enhances user interaction by allowing users to communicate with the chatbot using spoken commands. This feature expands the accessibility and convenience of the system, catering to a broader range of users with varying preferences and abilities. In addition to its conversational capabilities, the system prioritizes security and privacy, implementing robust user authentication mechanisms and secure data storage practices. By adhering to industry best practices and standards, the project aims to instill trust and confidence in users regarding the confidentiality and integrity of their data.User interface design and usability are also key considerations in the development process, with careful attention given to creating an intuitive and visually appealing interface. Through thoughtful design choices and user feedback, the system seeks to optimize the user experience and streamline the interaction flow, ensuring that users can easily navigate and utilize its features. Overall, the project endeavors to create an intelligent and user-friendly chatbot system that enhances communication and interaction between users and technology. By leveraging advanced technologies and methodologies, the system aims to provide personalized assistance and support to users, ultimately improving productivity, efficiency, and satisfaction in various domains and applications

Video Summarization for Marathi Language

The Video Summarization Platform using Python Flask is a comprehensive tool designed to summarize Marathi and English videos while providing summaries in Hindi, Marathi, and English languages. Leveraging machine learning and natural language processing (NLP) techniques, this platform offers a sophisticated solution for efficiently extracting key information from videos. The platform begins by transcribing the audio content of the video into text using automatic speech recognition (ASR) technology. This transcription process ensures that the platform can accurately analyze and summarize the video's content. Next, the text is translated into the target languages, namely Hindi, Marathi, and English, enabling users from diverse linguistic backgrounds to access the summarized content. To generate concise and informative summaries, advanced NLP algorithm is applied. This algorithm analyze the transcribed text to identify the most significant phrases, sentences, and concepts. By considering factors such as keyword frequency, semantic relevance, and context, the platform effectively distils the video's content into digestible summaries. Additionally, machine learning models are employed to classify the type of video content. These models are trained on diverse datasets encompassing various video genres and topics. By recognizing patterns and features within the video content, the platform can accurately categorize videos into distinct types, such as news, interviews, tutorials, or entertainment. The platform's user interface, powered by Python Flask, offers a seamless experience for users to upload videos, select their preferred language for summarization, and receive concise summaries in their chosen languages. The intuitive design ensures accessibility and ease of use, catering to both novice and advanced users. Overall, the Video Summarization Platform serves as a valuable resource for individuals seeking efficient ways to consume multimedia content. Whether for educational, informational, or entertainment purposes, this platform empowers users to access summarized video content in multiple languages, facilitated by cutting-edge machine learning and NLP technologies. Key Words: Transcription, Marathi-speaking users, Marathi YouTube videos, video content, transcription, summary, translation, Natural Language Toolkit (NLTK), content comprehension, user interaction data, past summaries, recommendation

Mapping Higher Education Internationalisation as a Research Space via Natural Language Processing (NLP) Techniques

The productivity of a specific research field hinges on the periodic examination of both the knowledge produced and the knowledge production activities. By harnessing the strength of traditional bibliometric analyses and a variety of Natural language processing (NLP) techniques, this study portrayed a holistic landscape of higher education internationalisation (HEI) research that incorporated time and region through a spatial lens. The findings reveal the field's evolution into establishment, significant regional variations in research focus, and the expansion of networks for disseminating knowledge. These factors collectively contribute to a diverse ‘lived’ space of HEI research. However, the dominance of Western-centric key concepts, theories, and discourses highlights a homogenous ‘conceived’ space, pointing to an underlying tension between these spaces. Despite these challenges, opportunities for breakthroughs exist. Additionally, the study underscores the immense potential of NLP techniques in facilitating the exploration of how research fields evolve, further enriching our understanding of HEI.

Sentimental analysis and prediction of socioeconomic disasters tweets by ML and regular expression

Sentiment analysis is a method of analyzing emotions and using text analysis techniques with natural language processing methods. Sentiment analysis uses data from various sources to identify the user’s attitude through different aspects. It is widely used for extracting opinions and recognizing sentiments, which helps Business organizations understand the user’s needs. This paper proposes a simple but compelling sentiment analysis method, showing the combined scores based on positive and negative words. Then, the tweets are categorized as Neutral, Negative, or Positive according to the scores. Sentiment analysis and opinion mining have grown significantly in the last decade. Different studies in this domain try to determine people’s feelings, opinions, and emotions about something or someone. The main objective of this analysis is to determine the sentiment of the review using a machine learning model and then compare the result with the manual review of the data. This would allow researchers to represent and analyze opinions objectively across different domains. A hybrid method that combines a supervised machine learning algorithm with natural language processing techniques is suggested for review analysis. This project aims to find the best model to predict the sentiment of the tweets on airlines. During the research process and considering various methods and variables that should be considered, we found that methods like naïve Bayes and random forest were not fully explored. The proposed system improves an effective and more feasible method for sentimental analysis using machine learning, multinomialNB, linear regression, and regular expression.

DEPRESSION DETECTION BY ANALYZING SOCIAL MEDIA POST OF USER: A REVIEW

In contemporary times, identifying depression at an early stage has become a critical issue within the field of psychology. Mental health problems, notably depression, afflict a significant portion of the global population, with an estimated 300 million individuals currently affected. Leveraging the vast amount of user-generated content on social media platforms, researchers are exploring ways to utilize this data to identify potential mental health issues in users. Depression remains a pressing concern in society and a topic of ongoing research globally. Despite numerous studies attempting to understand various mood disorders such as depression, anxiety, and stress using activity logs from devices like smartphones, accurately predicting depressive episodes remains challenging. Social media analysis has emerged as a popular approach to tackle this challenge. In this paper, we propose a system for assessing depression and detecting suicidal ideation by predicting suicidal tendencies based on the severity of depression. Our study aims to develop effective machine learning algorithms for identifying potential signs of depression in individuals through their social media posts. To achieve this, we trained and tested classifiers to discern whether a person exhibits signs of depression using features extracted from their social media activities. Various machine learning algorithms were employed to train and classify users into different levels of depression on a scale ranging from 0 to 100%. Data in the form of posts were collected and categorized into depressed or non-depressed using machine learning algorithms. Our primary contribution lies in exploring a range of features and their effectiveness in detecting the degree of depression. We aim to build a deep learning model capable of categorizing users with depression by learning from individual-level labels to infer post-level labels. By amalgamating the probabilities of post label categories, we can create temporal posting profiles to classify users with depression. Our findings reveal distinct variations in posting behaviors between depressed and non-depressed users, as indicated by the combined probabilities of post label categories. In our study, we utilized natural language processing (NLP) techniques, employing the BERT algorithm, to efficiently identify potential depression indicators in social media content. This approach offers a more accessible and effective means of detecting depression. Keywords— Machine Learning, NLP, BERT Algorithm, Depression, Classification, Social Media Post.