Application of Optimization and Machine Learning for Sentiment Analysis

Machine Learning (ML) is an Artificial Intelligence (AI) approach that allows systems to adapt to their environment based on past experiences. Machine Learning (ML) and Natural Language Processing (NLP) techniques are commonly used in sentiment analysis and Information Retrieval Techniques (IRT). This study supports the use of ML approaches, such as K-Means, to produce accurate outcomes in clustering and classification approaches. The main objective of this research is to explore the methods for sentiment classification and Information Retrieval Techniques (IRT). So, a combination of different machine learning algorithms is used with a dataset from amazon unlocked mobile reviews and telecom tweets to achieve better accuracy as it is crucial to consider the previous predictions related to sentiment classification and IRT. The datasets consist of user reviews ratings and algorithms utilized consist of K-Means Clustering algorithm, Logistic Regression (LR), Random Forest (RF), and Decision Tree (DT) algorithms. The amalgamation of each algorithm with the K-Means resulted in high levels of accuracy. Specifically, the K-Means combined with Logistic Regression (LR) yielded an accuracy rate of 99.98%. Similarly, the K-Means integrated with Random Forest (RF) resulted in an accuracy of 99.906%. Lastly, when the K-Means was merged with the Decision Tree (DT) Algorithm, the accuracy obtained was 99.83%.We exhibited that we could foresee efficient, effective, and accurate outcomes.

BackgroundIdentifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A2DS2, ISAN, PNA, and ACDD4 scores using the area under the receiver operating characteristic curve (AUC).ResultsAmong 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806–0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793–0.863, P = 0.040), ACDD4 (0.807, 95% CI 0.766–0.849, P = 0.041), A2DS2 (0.803, 95% CI 0.762–0.845, P = 0.013), ISAN (0.795, 95% CI 0.752–0.837, P = 0.009), and PNA (0.778, 95% CI 0.735–0.822, P < 0.001). All models demonstrated adequate calibration except for the A2DS2 score.ConclusionsThe ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.

Aspirin-exacerbated respiratory disease (AERD) is an acquired inflammatory condition characterized by the presence of asthma, chronic rhinosinusitis with nasal polyposis, and respiratory hypersensitivity reactions on ingestion of aspirin or other nonsteroidal anti-inflammatory drugs (NSAIDs). Despite AERD having a classic constellation of symptoms, the diagnosis is often overlooked, with an average of greater than 10 years between the onset of symptoms and diagnosis of AERD. Without a diagnosis, individuals will lack opportunities to receive effective treatments, such as aspirin desensitization or biologic medications. Our aim was to develop a combined algorithm that integrates both natural language processing (NLP) and machine learning (ML) techniques to identify patients with AERD from an electronic health record (EHR). A rule-based decision tree algorithm incorporating NLP-based features was developed using clinical documents from the EHR at Mayo Clinic. From clinical notes, using NLP techniques, 7 features were extracted that included the following: AERD, asthma, NSAID allergy, nasal polyps, chronic sinusitis, elevated urine leukotriene E4 level, and documented no-NSAID allergy. MedTagger was used to extract these 7 features from the unstructured clinical text given a set of keywords and patterns based on the chart review of 2 allergy and immunology experts for AERD. The status of each extracted feature was quantified by assigning the frequency of its occurrence in clinical documents per subject. We optimized the decision tree classifier's hyperparameters cutoff threshold on the training set to determine the representative feature combination to discriminate AERD. We then evaluated the resulting model on the test set. The AERD algorithm, which combines NLP and ML techniques, achieved an area under the receiver operating characteristic curve score, sensitivity, and specificity of 0.86 (95% CI 0.78-0.94), 80.00 (95% CI 70.82-87.33), and 88.00 (95% CI 79.98-93.64) for the test set, respectively. We developed a promising AERD algorithm that needs further refinement to improve AERD diagnosis. Continued development of NLP and ML technologies has the potential to reduce diagnostic delays for AERD and improve the health of our patients.

Social media platforms have become indispensable channels for communication and interaction, offering a wealth of data that can provide valuable insights into public sentiments. However, analyzing sentiment on these platforms poses significant challenges due to the diverse and unstructured nature of user-generated content. Traditional Natural Language Processing (NLP) techniques struggle to accurately classify sentiments expressed through text, images, emoticons, and multimedia elements. Moreover, the informal and nuanced language used in Electronic Word of Mouth (eWOM) further complicates sentiment analysis. In response, this paper explores the role of Artificial Intelligence (AI) in improving sentiment analysis on social media. By leveraging Machine Learning (ML) algorithms trained on large datasets, AI can enhance the accuracy and efficiency of sentiment classification, providing decision-makers with actionable insights into the sentiment landscape of social media. Keywords: Social media, Sentiment analysis, Artificial Intelligence, Machine Learning, Natural Language Processing, Electronic Word of Mouth.

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]. …

The concept of "quality of life" (QoL) encompasses all aspects of people's standard of living, including economic, social, or health-related factors, as well as their perceptions of their own lives. Although the growing application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in processing and modeling the diverse datasets associated with these domains, there remains a significant challenge in addressing different issues in QoL area of research and in fully harnessing these technologies to improve QoL research outcomes. Despite the technological advancements, current research endeavors often overlook the complex, multifaceted nature of QoL study. This oversight results in fragmented insights and leaves significant areas underexplored. In this work, we conducted a systematic literature review (SLR) to investigate the contribution of AI to QoL studies. For this, we collected 68 research works published between 2008 and 2022. This review covers a range of research questions about the objectives and methods of studies on QoL, the sources and types of data utilized, and the advancements made through the application of natural language processing (NLP), ML, deep learning (DL), statistical models, and semantic approaches. The goal of this review is to tackle the prevalent ambiguity in QoL dimensions, synthesize the research findings, and highlight the contributions, advancements, and most innovative approaches in the field. Moreover, we identify gaps and limitations in the current literature and suggest potential areas for future research, aiming to inspire more cohesive and comprehensive approaches to studying QoL using AI and ML techniques.

The advent of machine learning and natural language processing techniques, sentiment analysis has emerged as a promising approach for predicting stock prices using textual data such as news headlines and social media posts. This report focuses on the application of sentiment analysis for stock prediction using two popular machine learning algorithms, Random Forest, and Multinomial Naive Bayes, and the TFIDF technique for feature extraction. The dataset used in this study consists of news headlines from the financial news website, Financial Times, and the prediction task is to classify the direction of the stock price changes as either positive or negative. The purpose of this study is to evaluate the effectiveness of sentiment analysis for stock prediction and to compare the performance of Random Forest and Multinomial Naive Bayes algorithms.

Sentiment analysis, which stands for opinion mining, is a natural language processing (NLP) technique that involves identifying, extracting, and analyzing sentiments or opinions expressed in text data. The primary goal of sentiment analysis is to determine the sentiment polarity of a given piece of text, whether it is positive, negative, or neutral. This analysis can be applied to various types of content, such as product reviews, social media posts, customer feedback, and news articles. Sentiment analysis algorithms use machine learning and text classification to understand subjective information conveyed in text, helping businesses, organizations, and individuals gain insight into public opinions and emotions about specific topics, products, or services. In this study, we conducted sentiment analysis on a Bengali dataset. For feature extraction, we implemented the term frequency-inverse document frequency (TF-IDF) technique, and for feature selection, we applied an extra tree classifier approach. Subsequently, we trained our machine learning model, achieving an impressive accuracy rate of 92%.

Sentiment analysis has become increasingly important in understanding user perceptions of digital platforms. This study focuses on analyzing TikTok application reviews from the Google Play Store in Indonesia using machine learning techniques. The research aims to investigate sentiment distribution and compare the performance of three popular machine learning models: Random Forest, Support Vector Machine (SVM), and Naive Bayes. The study employed a comprehensive methodology involving data collection, preprocessing, feature extraction, and model evaluation. A dataset of 10,000 TikTok reviews was collected and preprocessed using techniques such as case folding, tokenization, and stopword removal. The sentiment labeling process categorizes reviews into positive, negative, and neutral sentiments based on user ratings. The TF-IDF algorithm was used for feature extraction, and the SMOTE technique addressed class imbalance. Results revealed a predominance of negative sentiment (53.5%), followed by neutral (32.1%) and positive (14.4%) sentiment. Model performance comparisons at different data sharing ratios (80/20 and 70/30) demonstrated that Random Forest and SVM consistently outperformed Naive Bayes. At the 80/20 ratio, Random Forest achieved the highest accuracy of 83.73%, highlighting its effectiveness in sentiment classification. The research contributes to the field of sentiment analysis and natural language processing by providing insights into user experiences with the TikTok application in Indonesia. The findings can guide application developers in understanding user perceptions and improving user experience.

Abstract: Automobile reviews provide a valuable opportunity for sentiment analysis in order to gain a better understanding of consumer opinions and improve product quality. As a result of the complexity of natural language and differences in user opinions, accurate and robust sentiment analysis by computer models remains challenging. Traditional machine learning algorithms such as Support Vector Machines (SVM) and Naive Bayes (NB) have been used for sentiment analysis on textual data. They can, however, be limited by the heterogeneity of user opinions and language variations. In contrast, deep learning models and natural language processing have shown great promise in sentiment analysis. In particular, the VADER (Valence Aware Dictionary and sentiment Reasoner) algorithm has been proposed as a powerful tool for sentiment analysis on social media texts, and has shown promising results in other domains as well. In this work, The aim is to achieve accurate sentiment analysis on automobile reviews by comparing the accuracy performance of SVM, NB, and the VADER algorithm.

Abstract: Artificial intelligence (AI), which has sped up the creation of intelligent conversational systems, or chatbots, has totally changed how people interact with computers. This investigation looks at how AI-powered chatbots are developed, designed, and used in a variety of sectors, such as customer service, healthcare, education, and e-commerce. It highlights how natural language processing (NLP), machine learning (ML), and deep learning techniques are combined to assist chatbots in understanding, interpreting, and effectively responding to consumer requests. The research examines typical frameworks and platforms for chatbot development in addition to language comprehension, emotional intelligence, privacy, and user engagement challenges. Furthermore, the effectiveness and adaptability of chatbots have significantly increased due to recent advancements in generative AI models, such as GPT and BERT. The final section of this paper examines future research directions, emphasizing AI chatbots' potential to create conversational agents that are more human-like, contextually aware, and emotionally intelligent. With the creation of intelligent chatbots that can comprehend and react to natural language, artificial intelligence (AI) has completely changed human–computer interaction. The design and deployment of an AI-based chatbot that mimics human speech in order to deliver prompt, precise, and context-aware responses is the main emphasis of this study. The chatbot interprets user inquiries, extracts pertinent data, and provides insightful responses in real time by utilizing Natural Language Processing (NLP) and Machine Learning (ML) techniques. The solution improves user engagement and conversational accuracy by incorporating deep learning models for entity recognition and intent detection. Additionally, the chatbot's design includes modules for response generation, dialogue management, and data preparation to guarantee dependability and flexibility across a range of disciplines, especially in healthcare applications..

In recent years, due to the rapid development of artificial intelligence (AI) and machine learning, its application has been widely used in many aspects of financial area, as well as significantly impacts financial market, institutions and regulation. The artificial intelligence technology brings enormous change to the entire financial industry, which creates a series of innovative financial services such as intelligent consultant, intelligent lending, monitoring and warning, and intelligent customer service as times required. In this paper, it aims to summarize the development and application of artificial intelligence and machine learning in financial system, as well as its impacts on macroeconomics and microeconomics. In the meantime, it is realised that a series of problems and risks were conducted by artificial intelligence during its use. Lastly, some suggestions and strategies are provided for reasonable usage of artificial intelligence in financial risk management, based on the financial risk management raised by artificial intelligence.

The rapid growth of social media platforms has significantly influenced human behavior, emotions, and mental well-being. With millions of users expressing their thoughts and feelings online, social media has become a valuable source of psychological insights. This project proposes an AI-based Psychology Analysis and Depression Detection System that leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques to identify early signs of depression from social media content. The system collects publicly available textual data such as posts, comments, and user interactions, and applies advanced text preprocessing methods including tokenization, sentiment analysis, and feature extraction. Machine learning models such as Logistic Regression, Support Vector Machines (SVM), and Deep Learning approaches like LSTM are trained to classify user mental states based on linguistic patterns, emotional tone, and behavioral indicators. Additionally, the project analyzes user engagement patterns, posting frequency, and sentiment trends over time to enhance prediction accuracy. The proposed model aims to provide early detection and psychological insights, enabling timely intervention and support for individuals at risk of depression. This AI-driven approach can assist mental health professionals, researchers, and organizations in understanding online psychological behavior while ensuring ethical data handling and user privacy protection.

In the recent decade, Natural Language Processing (NLP) has emerged as a important tool for extracting valuable insights from vast amounts of textual data. This work discusses the integration of NLP techniques with AI and robotics to enhance pattern mining capabilities. Using AI algorithms such as machine learning and deep learning, coupled with robotics for physical data gathering, enables the creation of sophisticated systems capable of understanding and interpreting human language in diverse contexts. In this study, we present a comprehensive framework that combines NLP intelligence with AI and robotics to extract meaningful patterns from textual data sources. We discuss the utilization of techniques such as sentiment analysis, named entity recognition, and topic modeling to analyze text data. We discuss about the integration of these NLP capabilities with AI algorithms for pattern identification and prediction. Moreover, the incorporation of robotics adds a tangible dimension to the pattern mining process, allowing for real-time data collection in various environments.

Mental health disorders represent a significant global health burden, often characterized by late diagnoses and inconsistent treatment outcomes. The integration of artificial intelligence (AI) with electronic health records (EHRs) offers a transformative approach to early diagnosis and intervention. This study presents the development of an AI-powered predictive model designed to enhance the diagnosis of mental health disorders by leveraging structured and unstructured data from EHRs. The model integrates natural language processing (NLP), machine learning (ML), and deep learning techniques to analyze clinical notes, patient histories, demographic data, and behavioral indicators. Our methodology involves preprocessing EHR datasets, extracting relevant features, and applying advanced algorithms such as random forests, support vector machines (SVM), and neural networks to build robust predictive models. Additionally, NLP techniques are used to process narrative text data, identifying critical indicators of mental health conditions such as depression, anxiety, bipolar disorder, and schizophrenia. The model is trained and validated using a large, anonymized EHR dataset, ensuring a high level of accuracy, precision, and recall in identifying at-risk individuals. Preliminary results demonstrate the model’s ability to outperform traditional diagnostic methods by identifying subtle patterns and risk factors often overlooked in standard clinical evaluations. Moreover, the integration of interpretability tools such as SHAP (SHapley Additive exPlanations) enables clinicians to understand the rationale behind each prediction, promoting trust and clinical applicability. This research underscores the potential of AI-driven tools in revolutionizing mental healthcare by enabling timely diagnosis, personalized treatment planning, and improved patient outcomes. However, the study also highlights challenges such as data quality, privacy concerns, algorithmic bias, and the need for interdisciplinary collaboration in deploying these systems responsibly. In conclusion, the proposed AI-powered predictive model demonstrates promise in augmenting mental health diagnostics using EHR data, paving the way for scalable and proactive mental healthcare delivery.