COMPARATIVE ANALYSIS OF HUMAN INTERACTION PATTERN MINING APPROACHES

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation.The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language.Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.

BackgroundQualitative research methods are increasingly being used across disciplines because of their ability to help investigators understand the perspectives of participants in their own words. However, qualitative analysis is a laborious and resource-intensive process. To achieve depth, researchers are limited to smaller sample sizes when analyzing text data. One potential method to address this concern is natural language processing (NLP). Qualitative text analysis involves researchers reading data, assigning code labels, and iteratively developing findings; NLP has the potential to automate part of this process. Unfortunately, little methodological research has been done to compare automatic coding using NLP techniques and qualitative coding, which is critical to establish the viability of NLP as a useful, rigorous analysis procedure.ObjectiveThe purpose of this study was to compare the utility of a traditional qualitative text analysis, an NLP analysis, and an augmented approach that combines qualitative and NLP methods.MethodsWe conducted a 2-arm cross-over experiment to compare qualitative and NLP approaches to analyze data generated through 2 text (short message service) message survey questions, one about prescription drugs and the other about police interactions, sent to youth aged 14-24 years. We randomly assigned a question to each of the 2 experienced qualitative analysis teams for independent coding and analysis before receiving NLP results. A third team separately conducted NLP analysis of the same 2 questions. We examined the results of our analyses to compare (1) the similarity of findings derived, (2) the quality of inferences generated, and (3) the time spent in analysis.ResultsThe qualitative-only analysis for the drug question (n=58) yielded 4 major findings, whereas the NLP analysis yielded 3 findings that missed contextual elements. The qualitative and NLP-augmented analysis was the most comprehensive. For the police question (n=68), the qualitative-only analysis yielded 4 primary findings and the NLP-only analysis yielded 4 slightly different findings. Again, the augmented qualitative and NLP analysis was the most comprehensive and produced the highest quality inferences, increasing our depth of understanding (ie, details and frequencies). In terms of time, the NLP-only approach was quicker than the qualitative-only approach for the drug (120 vs 270 minutes) and police (40 vs 270 minutes) questions. An approach beginning with qualitative analysis followed by qualitative- or NLP-augmented analysis took longer time than that beginning with NLP for both drug (450 vs 240 minutes) and police (390 vs 220 minutes) questions.ConclusionsNLP provides both a foundation to code qualitatively more quickly and a method to validate qualitative findings. NLP methods were able to identify major themes found with traditional qualitative analysis but were not useful in identifying nuances. Traditional qualitative text analysis added important details and context.

The field of study of Natural Language Processing (NLP) has developed over the past 50 years or so, producing an array of now mature technology, such as automatic morphological analysis, word sense disambiguation, parsing, anaphora resolution, natural language generation, named entity recognition, etc. The proliferation of large digital collections (evolving into Digital Libraries) and the emerging economic value of information demand efficient solutions for managing the information which is available, but which is not always easy to find. This chapter presents the requirements for handling documents in digital libraries and explains how existing NLP technology can be used to facilitate the task of document management.

We introduce Tasaheel-v2, an automated tool specifically developed for Arabic Natural Language Processing (NLP)and textual analysis tasks. This work is an extension to the first version, Tasaheel-v1, comprised of traditional NLP tasksincluding stemming, segmentation, normalization, named entity recognition, and part of speech tagging. Furthermore, itincluded cutting-edge analytic methods, such as specific emotion, polarity, linguistics, and domain-specific word tagging. In thisnew innovative version, Tasaheel-v2, we introduce additional benefiting utilities designed to provide assistance for the Arabicresearch community. We specifically integrate another Arabic-specific POS tagger, a sentiment analyzer, and English-to-Arabictranslation functions. We leverage the utilities provided in Tasaheel to develop a machine-learning model designed to identifyArabic phishing emails and provide a thorough textual analysis to capture deceptive cues used to detect phishing linguisticpatterns. This tool contributes to the Arabic research domain by providing assistive NLP functions and textual analysis featuresall in one tool

1 IntroductionOrganizations focus on automate their processes in order to improve efficiency, reduce costs, and/or reduce human beings' errors in an easy and rapid manner. Business process management (BPM) methods provide a solution to this issue. In this context, information systems like CRM, ERP, SCM, etc. have known an increasing demand. The main problem consists of the length of the business process specifications. If new regulations appear, these specifications must be adapted. Manually extraction of visual models is time consuming. During time, a series of solutions were proposed. The literature shows a crowd of approaches that extracts data models [1], [2], [3], and process models [4], [5] from Natural Language (NL) text. This paper aims to analyze the Natural Language Processing (NLP) techniques and tools used in order to provide different types of visual representations.On the last few years, approaches based on NLP have been developed in order to automate process conversion from NL text. NLP plays an important role in NL text analysis as NLP tries to understand speech and text as humans beings would do. Colloquialism, abbreviations or typos make this task a challenging one. NLP has the origins in 1950s when Alan Turing proposed the Turing test [6], by introducing the imitation game. Since then, the literature shows a plethora of NLP tools [7], [8], [9], [10] using several machine learning techniques, our focus being on those applied on data models and process models discovery from NL text, starting with the first language parser [11] to the actual ones like NLTk [7], [8], ANTLR1, etc.Linguistic analysis is closely tied to NLP. Liddy [12] highlights 7 levels of linguistic analysis: a) Phonetic or Phonological level: how words are pronounced, b) Morphological level: prefixes, suffixes and roots analysis, c) Lexical level: word level analysis including lexical meaning and Part-Of-Speech (POS) analysis, d) Syntactic level: grammatical analysis of words in a sentence, e) Semantic level: determining the possible meanings of sentences, f) Discourse level: interpreting structure and meaning for texts larger than a sentence, g) Pragmatic level: understanding the purpose of a language.Some of the problems approached by NLP are: POS tagging, parsing, Named Entity Recognition (NER), chunking, Semantic Role Labeling (SRL). Anaphora resolution [13] refers to the interpretation of the link between the anaphor and its antecedents.The remainder of the paper is organized as follows: Section 2 briefly outlines the NLP domain, describing the levels of linguistic analysis and the main NLP approaches of analyzing NL requirements. Section 3 focuses on data and process models extraction from NL text. This section makes an introduction to Object Oriented Analysis and Business Process Modeling and analyses the existing tools that discover data and process models from text. Subsequently, Section 4 summarizes the results of this work and the conclusions are drawn in Section 5.2NLPA detailed review on NLP is given in [14] and in [15]. Jones [14] divides the history of NLP into four phases: the first starts at the beginning of the 1940s and lasts to the late 1960s, the second begins from the end of 60s and lasts to the end of 70s, the third is represented by late 80s, where the fourth phase starts in the late of 90s. Next, we will detail each phase as they were defined in [14] and [15]. First phase treated machine translation issues, while the second focused on artificial intelligence. The third phase can be called grammatico-logical phase, which is followed by the lexical phase. A fifth phase is proposed in [16] where formal theories and statistical data are combined. Since this study was published first in 1994 and then it was re-organized in 2001 we can add the sixth phase: from 2000 until present where NLP techniques are combined in order to contribute to visual models extraction.Software requirements are usually written in NL which is asymmetric and irregular [17]. …

This research explored Korean EFL learners’ perspectives towards three different media modes in collaborative writing on Google Docs. To examine this, the study utilized sentiment analysis and word frequency in natural language processing (NLP). The results suggest how media modes can be effectively deployed in collaborative English writing.

Discourse analysis plays a significant role in understanding and interpreting the complexity of human language, providing valuable insights for improving natural language processing (NLP) systems. This study presents an in-depth review of discourse analysis in NLP, and its applications in various domains such as machine translation, question-answering systems, sentiment analysis, conversational bots, and information retrieval. By analyzing the structural and contextual aspects of language, discourse analysis enhances the NLP system’s ability to understand individual words and their meaning in cross-border conversational contexts. The review highlights how discourse analysis and learning techniques work together to improve the automated system's accuracy and efficiency. In addition to addressing significant challenges, including ambiguity, cultural variance, and conversational dynamics, the paper also suggests future research directions for exploring the possibilities of natural language processing in human-computer interactions. This work attempts to fill the gap between linguistic theory and computational applications by demonstrating how discourse analysis can revolutionize the development of more intelligent, contextually aware, and human-centered language technologies.

Natural language processing (NLP) is a subset of artificial intelligence demonstrating how algorithms can interact with individuals in their unique languages. In addition, sentiment analysis in NLP is better in numerous programs, including evaluating sentiment in Telugu. Several unsupervised machine-learning algorithms, such as k-means clustering with cuckoo search, are used to detect Telugu text. However, these techniques struggle to cluster data with variable cluster sizes and densities, slow search speeds, and poor convergence accuracy. This study developed a unique ML-based sentiment analysis system for Telugu text to address the shortcomings. Initially, in the pre-processing stage, the proposed Linear Pursuit Algorithm (LPA) removes words in white spaces, punctuation, and stops. Then, for POS tagging, this research proposed a Conditional Random Field with Lexicon weighting; following that, a Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC) is proposed to classify the sentiments in Telugu text. Consequently, the method we propose produces efficient outcomes in terms of accuracy, precision, recall, and f1-score.

Now-a-days, Social media platforms like Facebook, Twitter etc. are becoming more and more popular to express feelings and thoughts. People not only share their happy moments on these platforms but also share their feelings when they are extremely depressed. Analyzing these social media posts, one’s mental condition can be detected whether he/she is happy, sad or angry at a particular time using sentiment analysis in natural language processing. Most of the research in this field is based on English language and the accuracy of sentiment analysis from Bengali language is not very high. So, our purpose is to work on this field using Bengali dataset collected from different social media posts and make the sentiment detection more accurate so that this work can be used to build a system that can be used in the mental health sector of our country. In this research, we have first collected social media data. After applying different data preprocessing techniques, we have made a number of feature selection and extraction combinations. We have applied some basic and advanced machine learning and deep learning algorithms on each of these to find the best combination and algorithm by monitoring the highest achieved accuracy.

Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.

With the accelerated evolution of social networks, there is a tremendous increase in opinions by the people about products or services. While this user-generated content in natural language is intended to be valuable, its large amounts require use of content mining methods and NLP to uncover the knowledge for various tasks. In this study, sentiment analysis is used to analyze and understand the opinions of users using statistical approaches, knowledge-based approaches, hybrid approaches, and concept-based ontologies. Unfortunately, sentiment analysis also experiences a range of difficulties like colloquial words, negation handling, ambiguity in word sense, coreference resolution, which highlight another perspective emphasizing that sentiment analysis is certainly a restricted NLP problem. The purpose of this chapter is to discover how sentiment analysis is a restricted NLP problem. Thus, this chapter discussed the concept of sentiment analysis in the field of NLP and explored that sentiment analysis is a restricted NLP problem due to the sophisticated nature of natural language.

Purpose This study examines the evolution of education research in Kazakhstan from 1992 to 2024, a period marked by profound transformation, aiming to assess how scholarly output aligns with national education policy and broader developmental priorities. Design/methodology/approach A mixed bibliometric and natural language processing (NLP) approach was employed to analyze 956 English-language publications indexed in the Web of Science. The study combined quantitative analyses of publication trends, authorship and institutional collaboration with NLP techniques, including keyword co-occurrence, topic modeling and sentiment analysis, framed by a review of Kazakhstan’s education policy phases: post-Soviet foundational reforms and an era of global alignment. Findings Results reveal a substantial surge in research output, particularly after 2020, driven by a core set of highly productive authors and institutions. Thematic analyses indicate partial alignment with policy priorities, with multilingual education, digitalization and pedagogical innovation prominent, while inclusive education and social justice appear less substantively addressed. Collaboration networks display a core-periphery structure, with Nazarbayev University acting as the primary international and national hub. Originality/value This study offers the first comprehensive, longitudinal mapping of Kazakhstani education research, integrating bibliometric and NLP methods with policy analysis. It highlights both the rapid quantitative growth and the structural and thematic limitations of the field, providing a foundation for policy and institutional strategies aimed at fostering more diverse, critical and sustainable research.

IntroductionThe emergence of social media platforms like X has created a unique space for mental health discussions. This study aims to analyze the language and themes used in social media discussions to appreciate sentiments about PMS and PMDD by looking at a sample of the most popular tweets on platform X.ObjectivesWe hypothesize that this content can provide insight into public perceptions and guide educational campaigns.MethodsAn advanced Twitter/X search for “PMS” and “PMDD” was conducted, filtering for English content. The top 100 tweets for each search term were explored through two different sentiment analysis tools which include Dr. Daniel Soper’s Sentiment Analyzer Tool and Text2Data Application Programming Interface (API) Natural Language Processing (NLP) Analysis. Tweets were also analyzed using a word cloud generator to identify the most frequently used terms. Connecting words were eliminated from the final output.ResultsNegative sentiment was more prevalent than positive among the tweets for PMDD, with an overall sentiment analysis of an average of -24.3 per the Daniel Soper Sentiment Analyzer tool, suggesting a negative and serious tone. The most frequently appearing terms in these tweets were “month” (mentioned 17 times), “bad” (16), “love” (15), “feel” (14), “MAFS” (14), “support (14). Per Text2Data’s API NLP analysis, the top 150 words had a negative sentiment of -0.59 with a magnitude of 1.69. Negative sentiment was more prevalent than positive among the tweets for PMS, with an overall sentiment analysis of an average of -15.2 per the Daniel Soper Sentiment Analyzer tool, suggesting a somewhat negative and serious tone. The most frequently appearing terms in these tweets were “new” (11), “price” (10), “oxford” (9), “feel” (14), “people” (9), “want” (9). Per Text2Data’s API NLP analysis, the top 150 words had neutral sentiment of +0.15 with a magnitude of 1.59.ConclusionsThis study emphasizes a less negative sentiment associated with PMS compared to PMDD. It also highlights how more emotionally charged terms were used among tweets discussing PMDD compared to PMS. This may reflect public perception of the two conditions. Additionally, social media can be a way to gauge public interest and perception of medical topics.Disclosure of InterestNone Declared

In the age of knowledge, Natural Language Processing (NLP) express its demand by a huge range of utilization. Previously NLP was dealing with statically data. Contemporary time NLP is doing considerably with the corpus, lexicon database, pattern reorganization. Considering Deep Learning (DL) method recognize artificial Neural Network (NN) to nonlinear process, NLP tools become increasingly accurate and efficient that begin a debacle. Multi-Layer Neural Network obtaining the importance of the NLP for its capability including standard speed and resolute output. Hierarchical designs of data operate recurring processing layers to learn and with this arrangement of DL methods manage several practices. In this paper, this resumed striving to reach a review of the tools and the necessary methodology to present a clear understanding of the association of NLP and DL for truly understand in the training. Efficiency and execution both are improved in NLP by Part of speech tagging (POST), Morphological Analysis, Named Entity Recognition (NER), Semantic Role Labeling (SRL), Syntactic Parsing, and Coreference resolution. Artificial Neural Networks (ANN), Time Delay Neural Networks (TDNN), Recurrent Neural Network (RNN), Convolution Neural Networks (CNN), and Long-Short-Term-Memory (LSTM) dealings among Dense Vector (DV), Windows Approach (WA), and Multitask learning (MTL) as a characteristic of Deep Learning. After statically methods, when DL communicate the influence of NLP, the individual form of the NLP process and DL rule collaboration was started a fundamental connection.

Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset