Artificial intelligence, machine learning, and deep learning for sentiment analysis in business to enhance customer experience, loyalty, and satisfaction

SSRN Electronic Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

The integration of Artificial Intelligence (AI), Machine Learning (ML), and deep learning into sentiment analysis is revolutionizing how businesses enhance customer experience, loyalty, satisfaction. This research paper thoroughly reviews the latest advancements in AI ML, focusing on their application within business settings. By utilizing Natural Language Processing (NLP), allows to effectively understand respond emotions feedback. proliferation big data enables analyze extensive volumes interactions across diverse channels such as social media, reviews, support tickets real-time. AI-driven tools not only facilitate comprehension sentiments but also enable prediction trends early identification potential issues. predictive capability refine strategies, improve product offerings, personalize interactions, thereby enhancing overall experience. Moreover, continuous adaptation ML algorithms ensure that models remain accurate relevant new emerges. Current emphasize importance integrating AI-powered with relationship management (CRM) systems provide a comprehensive view preferences. combination AI, CRM essential for developing effective engagement strategies promote loyalty highlights significant impact experience identifies future directions this evolving field.

Language: Английский

---

Biomedical Research Enrichment Through Sentiment Analysis in Patient Feedback

Published: Jan. 13, 2025

This chapter consults the trajectory committed by utilizing patient feedback (PF) in wake of biomedical research through sentimental analysis (SA) natural language processing (NLP). PF has been compared to a gold mine for healthcare industry, delivering clinical efficacy, preserving quality, and overall insight into disorder. Analyzing these responses is vastly more time-consuming likely be subjective. However, employing SA can efficiently extract beneficial insights from this automating patients' positive, negative, or neutral sentiments. By systematically investigating thousands millions observations identify familiar themes, distinct concerns, satisfaction levels, researchers employ sentiments understand disease status, improve care, assist making intelligent decisions analyzing judgments. Miscellaneous traditional methods depend on surveys structured questionnaires accumulate that fails deliver preferred results terms sentiment. On other hand, studying sentiment data mixed social media posts electronic health records, work with unstructured furnish favorable results, allowing sweeten grade research. Eventually, discloses worthwhile wisdom enrich SA.

Language: Английский

---

AI and Geospatial Technologies for Climate Change Mitigation: Opportunities, Challenges, and Pathways to Sustainability

Procedia Computer Science, Journal Year: 2025, Volume and Issue: 259, P. 1346 - 1355

Published: Jan. 1, 2025

Language: Английский

---

Modified Aquila Optimizer with Stacked Deep Learning-Based Sentiment Analysis of COVID-19 Tweets

Electronics, Journal Year: 2023, Volume and Issue: 12(19), P. 4125 - 4125

Published: Oct. 3, 2023

In recent times, global cities have been transforming from traditional to sustainable smart cities. text sentiment analysis (SA), many people face critical issues namely urban traffic management, living quality, information security, energy usage, safety, etc. Artificial intelligence (AI)-based applications play important roles in dealing with these crucial challenges SA. such scenarios, the classification of COVID-19-related tweets for SA includes using natural language processing (NLP) and machine learning methodologies classify tweet datasets based on their content. This assists disseminating relevant information, understanding public sentiment, promoting practices areas during this pandemic. article introduces a modified aquila optimizer stacked deep learning-based COVID-19 Classification (MAOSDL-TC) technique The presented MAOSDL-TC incorporates FastText, an effective powerful representation approach used generation word embeddings. Furthermore, utilizes attention-based bidirectional long short-term memory (ASBiLSTM) model sentiments that exist tweets. To improve detection results ASBiLSTM model, MAO algorithm is applied hyperparameter tuning process. validated benchmark dataset. experimental outcomes implied promising compared models terms different measures. improves accuracy interpretability prediction.

Language: Английский

---

Fake news detection models using the largest social media ground-truth dataset (TruthSeeker)

International Journal of Speech Technology, Journal Year: 2024, Volume and Issue: 27(2), P. 389 - 404

Published: June 1, 2024

Language: Английский

---

An NLP Approach to Enrich Biomedical Research Through Sentiment Analysis of Patient Feedback

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 155 - 188

Published: Oct. 4, 2024

This chapter consults the trajectory committed by utilizing patient feedback (PF) in wake of biomedical research through sentimental analysis (SA) natural language processing (NLP). PF has been compared to a gold mine for healthcare industry as it delivers clinical efficacy and preserves quality. Analyzing these responses is vastly more time-consuming subjective. SA employment can efficiently extract beneficial insights from this automating patients' positive, negative, or neutral sentiments. By systematically examining millions remarks identify familiar themes, distinct concerns, satisfaction levels, researchers employ sentiments understand disease status assist making intelligent decisions. work with structured unstructured sentiment data mixed social media posts electronic health records produce favorable results, allowing improve research. Eventually, uncloses worthwhile wisdom enrich employing SA.

Language: Английский

---

Insights from Machine Learning Models: Sentiment Trends on X (Formerly Twitter)

International Journal of Electronics and Communication Engineering, Journal Year: 2024, Volume and Issue: 11(12), P. 154 - 163

Published: Dec. 31, 2024

X (formerly Twitter) has long been a platform that allows users to share their thoughts and beliefs vent more negative feelings on plethora of subjects. In an age dominated by social media, where people online lay emotions opinions bare, the ability utilize natural language processing methods extract assess sentiments from tweets become crucial. Using machine learning models like Random Forest Classifier, Logistic Regression, Naïve Bayes, which produced encouraging findings, study technique includes data gathering, preprocessing, feature extraction, sentiment categorization. After performing thorough research analysis tweets, paper delves into possible ramifications national security surveillance perspective.

Language: Английский

---

AI and Database Management for Organizational Transformation With Insights From Twitter Data

Journal of Database Management, Journal Year: 2024, Volume and Issue: 35(1), P. 1 - 25

Published: Nov. 9, 2024

This paper explores the role of AI and database management in organizational transformation using insights from Twitter data. By analyzing 30,000 English-language tweets with methods such as word analysis, topic modeling, network sentiment emotion study reveals a strong correlation between digital transformation. The findings show positive optimism about AI's potential. research highlights importance social influence, perceived trust, awareness adoption, offering valuable for researchers practitioners. Despite relying on data, provides practical guidance leveraging efforts.

Language: Английский

---