Sentiment Analysis - Science topic

This study introduces a new method for mining mobile app reviews using the "scrapper" package and sophisticated ML algorithms. The goal was to improve app marketing and development by gleaning useful information from a massive database of user reviews. This was accomplished by classifying the retrieved reviews as either favorable, negative, or neut...

The rapid evolution of artificial intelligence and machine learning has revolutionized the landscape of algorithmic trading, particularly in the realm of news and social media analysis. This comprehensive study examines the development, implementation, and impact of AI-driven trading algorithms that leverage real-time news and social media data to...

Existing research on constructional professional attitudes towards fire safety and evacuation has predominantly employed traditional methodologies. While these methods have provided valuable insights, they are limited in their ability to capture the full spectrum of the stakeholders. Moreover, a significant gap exists in the literature regarding th...

Accurately detecting the emotional tone in a conversation, such as in chat interactions, can be crucial for effective dialogue. Therefore, automated sentiment analysis is a key component of AI-driven dialogue systems, particularly in higher education, where it helps support learning motivation. In the SMARTA (Student Motivation and Reflective Train...

Drawing upon previous research, this paper creatively combines different methods to conduct text analysis of cancer diaries related to cancer treatment on Chinese social media Weibo, and explores the sentiment, adoption of cancer frames, and psychological aspects of language. Initially, sentiment analysis technology is employed to investigate the s...

This paper presents a framework for collecting large datasets of hotel reviews (e.g., from Booking.com and TripAdvisor) and performing useful analytics from the data collected. This approach automates data collection, reduces manual effort, enhances data cleaning, and standardizes data processing. We compiled extensive datasets of 607,451 reviews f...

The literature has explained how technology adoption and usage is affected by many socio-technical issues and how the emergence and evolution of networks is key for innovation. However, empirical analysis of interactions between leadership networks and new artificial intelligence-driven technologies is scarce. To shed new light on leadership commun...

The volatile price changes of shallots are a challenge in controlling their prices. The fluctuation in the price of shallots is always reported in the media because it affects people's lives. The news is released online via the internet and has beneficial information so it can be utilized. This study aims to provide a comparative analysis of foreca...

Customer feedback provides essential insights into service quality, brand perception, and operational efficiency. However, manually analyzing large volumes of feedback is inefficient and prone to bias. Sentiment analysis-a Natural Language Processing (NLP) technique-automatically identifies the emotional tone of textual feedback, aiding in timely a...

Cross-cultural disputes, both in digital and real-world settings, pose unique challenges due to linguistic diversity, cultural norms, and communication styles. Traditional methods of conflict resolution often fall short in multilingual environments. This paper explores the potential of Multilingual Natural Language Processing (NLP) in enabling fair...

In the digital era, business platforms have considerably evolved towards online stores on the internet. Through the internet-based platform, customers can easily buy products through their smartphones and receive delivery at the place without going to the shopping mall. On the other hand, the disadvantage of these platforms is that customers need t...

The existing literature on human–computer interactions is rich in studies on how consumers interact with brands on social media. However, there is a gap in the research on consumers' responses to the language style of social media branded messages. Therefore, this study aims to analyze e‐retailers' Twitter (now X) posts through text analysis to ide...

Customer feedback serves as a critical input for service enhancement, yet the vast volume and unstructured nature of such data present challenges for timely resolution. Sentiment analysis, a subfield of Natural Language Processing (NLP), offers an efficient mechanism to assess customer emotions and prioritize complaints. This paper explores the app...

In the contemporary digital era, multimedia platforms, such as social media, online comment sections, and forums, have emerged as the primary arenas wherein users articulate their sentiments and viewpoints. The copious volume of textual data generated by these platforms harbors a wealth of emotional insights, which are paramount in comprehending us...

Natural Language Processing (NLP) is a transformative discipline within artificial intelligence that empowers machines to understand, interpret, and generate human language. As the foundation of modern conversational interfaces, NLP plays a pivotal role in bridging the interaction gap between people and digital systems. From chatbots and voice assi...

Aspect sentiment triplet extraction (ASTE), which aims to extract aspect terms, opinion terms, and sentiment polarity from textual comments, is a crucial task in aspect-based sentiment analysis. Most existing approaches focus on leveraging contextual information while neglecting the effective utilization of syntactic structures within the text. To...

This study offers a detailed literature review and bibliometric analysis of cryptocurrency, virtual digital assets (VDA), and distributed ledger technology (DLT)-based digital currencies. We analyze current research and publishing trends, particularly in forecasting cryptocurrency price volatility. The paper categorizes the development and maturity...

Natural Language Understanding (NLU), a critical subfield of Natural Language Processing (NLP), is revolutionizing the way humans interact with machines. Unlike earlier systems based on rigid commands and keyword inputs, modern NLU technologies enable machines to grasp the intent, context, and semantics behind human language. This leap is powering...

This study presents the development and deployment of a sentiment analysis model based on Natural Language Processing (NLP) techniques in the context of public service evaluation. The model was implemented to automate the classification of citizens’ feedback on services provided by the EXPRESSO program in the State of Goiás, Brazil. Before implemen...

Recent climate-related protests by social movements such as Extinction Rebellion, Just Stop Oil , and others have included actions like defacing artwork and gluing oneself to objects and streets. Using sentiment analysis and frame detection models, we analyze a corpus of all available English-language news articles in LexisNexis, with the first rec...

This study significantly contributes to the sphere of educational technology by deploying state-of-the-art machine learning and deep learning strategies for meaningful changes in education. The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-...

Social media has become a primary platform for users to express their opinions and emotions through comments. However, the large volume of user-generated content makes manual moderation inefficient. Sentiment analysis presents a viable solution for automatically identifying public opinions. While algorithms such as Naive Bayes and Support Vector Ma...

As the demand for high-quality, scalable, and cost-efficient data collection grows across research domains, traditional survey methodologies continue to face significant challenges, including declining response rates, sampling biases, and deteriorating response quality. This study investigates the potential of Artificial Intelligence (AI) powered a...

Sentiment analysis on social media is becoming an important approach in understanding public opinion towards an event. Twitter, as a microblogging platform, generates a large amount of data that can be utilized for this analysis. This study aims to evaluate and compare the performance of three classification algorithms, namely Support Vector Machin...

Small and medium-sized enterprises (SMEs) are pivotal to global economic development, employment generation, and innovation. However, these businesses are often disproportionately vulnerable to financial instability due to limited resources, reduced access to credit, and heightened exposure to market fluctuations. In this context, effective risk as...

The use of machine learning to analyze sentiments has attained considerable interest in the past few years. The task of analyzing sentiments has becfigome increasingly important and challenging. Due to the specific attributes of this type of data, including length of text, spelling errors, and abbreviations, unconventional methods and multiple step...

With the growing integration of artificial intelligence in healthcare, sentiment analysis has emerged as a valuable tool for understanding patients' mental health through their digital expressions. However, the sensitive nature of mental health data raises critical concerns about user privacy and data protection. This review presents a comprehensiv...

We predict gross box office revenue of American movies released after 1970 using textual and quantitative data extracted from Internet Movie Database and Rotten Tomatoes. We leverage different machine learning models and sentiment analysis to analyze how film performance is related to different movie metrics, such as popularity score, runtime, and...

In today's digitally-driven world, the demand for personalized and context-aware recommendations has never been greater. Traditional recommender systems have made significant strides in this direction, but they often lack the ability to tap into the richness of conversational data. This paper represents a novel approach to recommendation systems by...

Social media platforms like X (formerly Twitter) play a crucial role in shaping public discourse and societal norms. This study examines the term Sessiz Istila (Silent Invasion) on Turkish social media, highlighting the rise of anti-refugee sentiment amidst the Syrian refugee influx. Using BERTurk and the TREMO dataset, we developed an advanced Emo...

With the growing concerns around data privacy, traditional centralized approaches to sentiment analysis face significant challenges, particularly regarding the handling of sensitive user data. This paper proposes an end-to-end pipeline for privacy-preserving sentiment analysis using Federated Transformers. The pipeline leverages the power of federa...

Aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to identify the sentiment polarity associated with specific aspects in a sentence. To address ABSA tasks, state-of-the-art methods incorporate attention mechanisms with Abstract Meaning Representation (AMR) to model the relationships between specific aspects and their contextua...

With the growing reliance on machine learning models for sentiment analysis in mental health applications, privacy concerns have become increasingly important, particularly regarding sensitive personal data. This paper proposes an innovative approach to privacy-enhanced mental health sentiment analysis by integrating data obfuscation techniques wit...

Lampung Province is a province that has so much natural beauty, this also makes Lampung Province one of the tourist destinations that are visited by many domestic and foreign tourists so that there is a problem, namely the many negative comments that are not in accordance with reality affect the number of tourist visits to Lampung Province because...

RESUMOEste estudo investiga a possível ocorrência do efeito copycat em casos de feminicídio no Distrito Federal entre os anos de 2015 e 2025. A motivação decorre da hipótese de que a ampla divulgação midiática de feminicídios consumados poderia influenciar o aumento de casos tentados nos períodos subsequentes. Para tanto, analisaram-se 925 registro...

This article presents the development of a method, mechanism, module, and algorithm for the automatic analysis, real-time visualization, and intelligent resolution of citizen appeals using artificial intelligence (AI) and geo-mapping technologies. The article outlines the key directions and technologies involved in organizing interactive citizen ap...

Sentiment analysis of content is highly essential for myriad natural language processing tasks. Particularly, as the movies are often created on the basis of public opinions, reviews of people have gained much attention, and analyzing sentiments has also become a crucial and demanding task. The unique characteristics of this data, such as the lengt...

Mental health sentiment analysis plays a critical role in identifying emotional states, detecting psychological distress, and providing early intervention through digital platforms. Traditional natural language processing (NLP) techniques often struggle to accurately capture the complex linguistic patterns inherent in mental health-related text. Bi...

This paper introduces H-STGNN-ODE-DA, a novel model for voice sentiment analysis that combines multi-scale acoustic feature extraction, hierarchical graph neural networks (GNNs), Neural Ordinary Differential Equations (Neural ODEs), and domain-adversarial adaptation. Designed to enhance accuracy and robustness under real-world conditions, the model...

We introduce \textbf{LAMP} (\textbf{L}inear \textbf{A}ttribution \textbf{M}apping \textbf{P}robe), a method that shines light onto a black-box language model's decision surface and studies how reliably a model maps its stated reasons to its predictions through a locally linear model approximating the decision surface. LAMP treats the model's own se...

Sentiment analysis, a natural language processing (NLP) technique, has emerged as a promising tool for understanding and evaluating human emotions, particularly in the context of mental health. By analyzing textual data from social media, online forums, and clinical notes, sentiment analysis can provide valuable insights into individuals' emotional...

The landscape of sentiment analysis applications in Indonesia is on the rise with the many published papers on the subject over the years. The need to predict sentiment coincides with the rise of social media and how the public uses it to express sentiments toward an interesting topic. The lack of tools for working with the Indonesian language has...

This paper presents an end-to-end suite for multilingual information extraction and processing from image-based documents. The system uses Optical Character Recognition (Tesseract) to extract text in languages such as English, Hindi, and Tamil, and then a pipeline involving large language model APIs (Gemini) for cross-lingual translation, abstracti...

With the rapid development of social media platforms, public opinion analysis and trend forecasting have become key decision-making capabilities for governments and enterprises. In this study, a real-time public opinion monitoring system is built by integrating multimodal AI technology, in which the convolutional neural network is responsible for e...

With the rapid growth of artificial intelligence and machine learning, predicting stock market trends has become an increasingly important and complex task. In this paper, we propose a novel stock index prediction model, SA-BERT-LSTM, based on financial text sentiment analysis. This model integrates the BERT (Bidirectional Encoder Representations f...

Sentiment analysis in Persian texts poses a persistent challenge in the field of natural language processing (NLP) due to the unique linguistic features and structural complexities of the language. Existing methods for sentiment analysis often demand substantial computational resources because of their reliance on complex models with numerous param...

The demand for movie reviews sentiment analysis is growing rapidly nowadays. This study focuses on the development of advanced text classifiers to address complex classification tasks and proposes three models. The first model utilizes 6 encoder layers to capture information from texts and the second analyzes data using pretrained parameters of bid...

Minnan nursery rhymes (MNRs), an integral part of Minnan intangible cultural heritage (ICH), are shared by southern Fujian, Taiwan, and overseas Chinese communities. Preserving and analyzing MNRs, especially their emotional evolution over time, is crucial but challenging due to shifting cultural contexts. Traditional sentiment analysis methods ofte...

As digitization continues to expand nowadays, the accurate capture and comprehension of public sentiment on social media has become vital for various stakeholders such as government, businesses, and researchers. In this paper, we aim to perform sentiment analysis on Tweets and explore effective methods to classify sentiment into joy, sadness, anger...

This article examines public opinion on social media regarding transgender athlete Imane Khelifs participation in womens boxing at the 2024 Paris Olympics. By synthesizing existing literature and empirical research, the study identifies key factors that influence public opinion, such as exposure to trans narratives and ideological orientation. The...

Cross-domain sentiment analysis is a fundamental challenge in NLP with applications in diverse areas such as product reviews, customer feedback, and social media monitoring. In this paper, we propose a comprehensive approach for Cross-Domain Aspect-Based Sentiment Analysis (CD-ABSA) that integrates aspect extraction and sentiment classification. Le...

To determine how physician adherence to recommended practices for discussing Down syndrome (DS) impacts patient experiences, and which of these recommendations most correlate with positive prenatal patient experiences. Online surveys were distributed to mothers of children with DS born between 2016–2021. The descriptions of prenatal experiences wer...

The prevalent routine of the World Wide Web (WWW) has unveiled unique means for society to rapidly develop its moods and sentiments. It also serves as a highly content-rich platform where users may experience diverse opinions that can influence their decision-making process. When expressing opinions, both words and texts carry meanings that depend...

Amid growing global attention to occupational health and safety, the construction industry faces critical challenges associated with engineered stone, which emits high concentrations of respirable crystalline silica during processing and has been linked to severe lung diseases. In response, Australia enacted a comprehensive nationwide ban on engine...

With the unprecedented success of "Ne Zha 2" in 2025, China's animation film industry has achieved a historical breakthrough admist the global cinema markets exponential growth. As box office prediction emerges as an essential tool for investment optimization and creative development, this study establishes a machine learning-based prediction frame...

Scenic area attractiveness is a core factor in urban tourism development. Developments in social media and multi-source spatiotemporal data provide a basis for studying complex tourist behaviors, overcoming the limitations of traditional interview survey data. This study combines point of interest (POI), mobile signaling, and microblog check-in dat...

بسم الله الرحمن الرحيم المقدمة الحمد لله رب العالمين، والصلاة والسلام على سيدنا محمد، خاتم النبيين وإمام المرسلين، وعلى آله وصحبه أجمعين. أما بعد، في عصر يتسم بالتقدم العلمي والتكنولوجي، أصبحت الحاجة ملحة لربط العلوم العقلية بالعلوم ما وراء الطبيعة واهمها النصوص الدينية، لا سيما ايات وسور من القرآن الكريم، الذي يُعد مصدرًا للمعارف الإلهية والإنساني...

Few studies have explored how emotional expressions regarding various aspects affect review helpfulness despite having emotionally charged online reviews of various topics or aspects. The ultimate purpose of this study is to provide a comprehensive understanding of how different emotional expressions in online reviews influence their perceived help...

Multimodal sentiment analysis is an important research direction in the field of natural language processing and artificial intelligence, aiming to improve the accuracy of emotion recognition by using multimodal data such as text, speech and image. Under the background of today's big data era, the technology of multi-modal sentiment analysis is bec...

Citation: Gao, J.; Agarwal, R.; Garsole, P. AI Testing for Intelligent Chatbots-A Case Study. Software 2025, 4, 12. https://doi. Abstract: The decision tree test method works as a flowchart structure for conversational flow. It has predetermined questions and answers that guide the user through specific tasks. Inspired by principles of the decision...

This study investigates the evolution of public attitudes toward China in the post-COVID-19 pandemic period, specifically before and after the implementation of Chinas Visa-Free Transit Policy. Therefore, this paper collected and analyzed 31,395 comments from the #China Travel and #ChinaVlog on YouTube, spanning from May 5, 2023, to the present, re...

This paper explores the design of an aspect-based sentiment analysis system using large language models (LLMs) for real-world use. We focus on quadruple opinion extraction -- identifying aspect categories, sentiment polarity, targets, and opinion expressions from text data across different domains and languages. Using internal datasets, we investig...

This research aims to analyze the sentiment of tourist reviews on the TripAdvisor platform towards a luxury resort in Bali by utilizing the Naïve Bayes classification method. The review data is analyzed to identify positive, negative, and neutral sentiments. Three variants of Naïve Bayes algorithm (GaussianNB, MultinomialNB, and BernoulliNB) were i...

Recent advancements in Large Language Models (LLMs) have enabled their adoption across a wide range of business applicationshave facilitated their widespread adoption in diverse business applications. ChatGPT, in particular, exemplifies these advancements with its exceptional capabilities in interpreting contextual and complex information and gener...

This paper presents our approach to sentiment analysis for Twi, a low-resource African language. We developed two types of systems: a translation-based system and an end-to-end system. These systems leverage various popular large-scale language models (LLMs), such as BERT and its multilingual variants, and their performances were compared. Our eval...

This article examines the application of advanced artificial intelligence techniques to enhance financial forecasting accuracy and improve investment decision-making processes. By integrating Long Short-Term Memory (LSTM) networks and Recurrent Neural Networks (RNNs) with economic indicators and real-time market sentiment analysis, the article deve...

Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a widely used clustering algorithm renowned for its ability to identify clusters of arbitrary shapes and detect noise. However, its reliance on fixed parameters, such as the minimum number of points (MinPts) and the epsilon radius (epsilon), makes it sensitive to variations in...

Predicting cryptocurrency prices is challenging due to market volatility and external influences like social media sentiment. This study integrates Twitter sentiment analysis with deep learning models (LSTM, GRU, Bi-LSTM, and Temporal Attention Model) to enhance Bitcoin price forecasting. Sentiment features were extracted using VADER and RoBERTa, w...

Am.I. lies at the intersection of artificial intelligence, robotics, and the humanities, integrating large language models (LLMs) with responsive robotic systems. The project features a reactive robotic head that engages in real-time philosophi-cal dialogues using GPT models. This robotic head converses with a parallel AI system, generating dynamic...

In this paper, we expand our exploration the usefulness of sentiment polarity as a stylistic feature for authorship attribution. We briefly describe our methodology and present the results from experiments using three English prose corpora. A comparison with attribution performed using traditional stylistic features on the same corpora is presented...

Large Language Models (LLMs) employ deep learning algorithms to generalize patterns in data. Applying these LLMs to classification tasks can reduce the required labor and time. The research aims to fine-tune the LLM Llama 3.1 to correctly identify whether a chosen text message exhibits a positive or negative emotion. The goal of this procedure is t...

This study investigates the emotional responses of Black parents in Missouri to racial violence, revealing three distinct types of emotional responses: negative emotions (feelings that induce distress or discomfort), neutral emotions (feelings that are neither positive nor negative), and positive emotions (feelings that induce joy or satisfaction)....

1 This study investigates the robustness of 2 Transformer-based, Deep Learning models, 3 specifically BiLSTM and BERT, against 4 linguistic noise in social media sentiment 5 analysis tasks. Using Twitter datasets from 6 SemEval (2015 and 2017), the performance 7 of a traditional BiLSTM model trained on 8 GloVe embeddings is compared with a pre-9 tu...

Relation extraction (RE) is a fundamental task in natural language processing, and it is of crucial significance to applications such as structured search, sentiment analysis, question answering, and summarization. RE involves the identification of relations between entities, which is a challenging task for languages with low digital resources. Thi...

The Consumer Price Index (CPI) is a key economic indicator used by policymakers worldwide to monitor inflation and guide monetary policy decisions. In Korea, the CPI significantly impacts decisions on interest rates, fiscal policy frameworks, and the Bank of Korea’s strategies for economic stability. Given its importance, accurately forecasting the...

Large language models (LLMs) have achieved state-of-the-art performance across a wide range of natural language processing (NLP) tasks. With their high inference computational costs, deployment is extremely challenging, especially to resource-constrained environments. Dynamic pruning methods, because they are efficient, are likely to assign uniform...

This study aims to explore customers' perception toward an Indonesian handmade shoes and brand image created after seeing the review made by a foreign customer who happens to be a Youtuber. This study is Adopting a qualitative sentiment analysis method by analyzing comments from YouTube reviews that have been cleaned up using Atlas.ti. The study in...

With the advent of Web 2.0 and popularization of online shopping applications, there has been a huge upsurge of user generated content in recent times. Leading companies and top brands are trying to exploit this data and analyze the market demands and reach of their products among consumers using opinion mining. Sentiment analysis is a hot topic of...

This comprehensive article examines the evolution and future trajectory of conversational artificial intelligence, with a particular focus on Contact Center AI (CCAI) as a specialized implementation transforming customer service operations. The narrative traces the technological progression from rudimentary rule-based chatbots to sophisticated neur...

The role of artificial intelligence in daily life is constantly advancing and has become an important topic of discussion. With music and its lyrics being a vehicle to express topics of society, this paper investigates how artificial intelligence is perceived by musicians and reflected in their songs. By analyzing the lyrics of over 1200 songs over...

The proliferation of social networking platforms has generated a substantial volume of user-generated content, posing significant challenges for text classification due to its diverse nature. Sentiment analysis or opinion mining, is crucial for extracting insights from user opinions and emotions regarding various entities and events. This research...

Objectives Globally, there has been a rapid increase in the availability of online gambling. As online gambling has increased in popularity, there has been a corresponding increase in online communities that discuss gambling. The movement of gambling and communities interested in gambling to online spaces presents new challenges to harm reduction....

In this digitized world with e-markets, customer review holds an utmost position in consumer behavior and purchase pattern. This project, "Product Review Classification Using Sentiment Analysis," presents a web application performing the following: by viewing product reviews, users receive sentiment-based recommendations. The system uses a pretrain...

Protecting lives and livelihoods during volcanic eruptions is the key challenge in volcanology, conducted primarily by volcano monitoring and emergency management organisations, but it is complicated by scarce knowledge of how communities respond in times of crisis. Social sensing is a rapidly developing practice that can be adapted for volcanology...

There was a period when economists and other scientists were deeply captivated by the anticipation of speculative exchange costs. Political factors, economic factors, leadership changes, investor perception, and a host of other attributes all made stock prices extremely volatile and difficult to predict. It has been proven to be inadequate to attem...

The Israel-Palestine conflict has triggered a global consumer movement, including a widespread boycott of Israeli-affiliated products in Indonesia. As this campaign gains momentum on digital platforms like X (formerly Twitter), understanding public sentiment becomes crucial—not only for gauging public opinion but also for anticipating potential soc...

The emergence of global health crises, such as COVID-19 and Monkeypox (mpox), has underscored the importance of understanding public sentiment to inform effective public health strategies. This study conducts a comparative sentiment analysis of public perceptions surrounding COVID-19 and mpox by leveraging extensive datasets of 147,475 and 106,638...

Crisis Management Systems (CMSs) serve as fundamental elements in emergency response, enabling control over natural disasters, pandemics, and cybersecurity incidents. CMSs often perform inadequately when handling unstructured emergency communication and delayed messages. Frequent emergencies, along with their complex nature, necessitate response ap...

This study investigates common health problems and online healthcare seeking behavioral patterns of individuals using a large-scale online consultation dataset extracted from well-known healthcare platforms. By using text mining techniques, including tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and topic model...

This research reviews state-of-the-art machine learning techniques for sentiment analysis in social media, tracing the evolution from traditional models like Support Vector Machines (SVM) and Naïve Bayes to advanced deep learning and transformer-based architectures. The evolution of the field is marked by the adoption of methods that leverage conte...

In order to understand the subconscious of a society, it may sometimes be necessary to examine proverbs. Because of their proverbs, they are simply like rational delegate of a society or an ethnic group. This delegate not only showing mental structure of society but also contribute the safekeeping and continuity of culture. Hence Kyrgyz people have...

This paper uses quantitative research methods to explore the differences in the impact of virtual influencers on different consumer groups in the context of technological integration and innovation. The study uses DBSCAN clustering technology to segment consumers and combines social media behavior analysis with purchase records to collect data to i...

Objective: The goal of this study is to enhance forecast accuracy by leveraging advanced deep learning (DL) methods, specifically Generative Adversarial Networks (GAN) and Long Short-Term Memory (LSTM) networks. Methods: Using historical stock data and 32 influencing features, GANs and LSTMs are trained to predict the stock price using the RMSE, wi...

This paper reviews the data analytics and machine learning applications in enhancing the personalization of digital marketing communications within the technology sector. Our review focuses on key areas such as customer segmentation, predictive analytics applications, real-time data processing, and behavioral and sentiment analysis. Using an explor...

This paper investigates the function of Artificial Intelligence (AI) and Machine Learning (ML) in augmenting crisis communication, analyzing their applications, efficacy, and obstacles to their implementation. Utilizing a mixed-methods approach that encompasses case studies of significant crises such as the COVID-19 pandemic, Hurricane Katrina, and...