Agentforce 2.0: Transforming Business Processes Through AI-Driven Automation (Published)
This article examines Agentforce 2.0, Salesforce’s advanced AI-driven automation platform that transcends conventional automation capabilities by integrating natural language processing and dynamic decision-making algorithms. The platform represents a fundamental paradigm shift in business process architecture, enabling organizations to reimagine core functions through contextually-aware intelligent agents capable of managing complex, multi-stage processes with minimal human intervention. The technological framework combines sophisticated multi-tiered infrastructure with fifth-generation enterprise automation capabilities, allowing for unstructured data processing, adaptive learning, and contextual decision-making. Implementation success depends on structured methods encompassing technological, organizational, and human dimensions, with phased deployment methods demonstrating superior outcomes. Measuring impact requires comprehensive frameworks addressing operational efficiency, customer experience, and financial dimensions. Agentforce 2.0 delivers quantifiable benefits across lead management, customer service, administrative tasks, and customer engagement, creating sustainable competitive advantage through enhanced operational performance and superior customer experiences. The platform’s ability to transform business processes while maintaining high quality standards positions it as a cornerstone technology for organizations seeking strategic automation solutions in an increasingly competitive business landscape.
Keywords: Artificial Intelligence, Intelligent automation, business process transformation, natural language processing, sentiment analysis
Leveraging AI/NLP to Combat Health Misinformation and Promote Trust in Science (Published)
The proliferation of health misinformation online poses a significant threat to public well-being and erodes trust in scientific consensus. Artificial Intelligence and Natural Language Processing offer powerful tools for identifying and countering such misinformation across digital platforms. By examining techniques like concept clustering and bot detection as applied to e-cigarette discussions on social media, this paper illuminates how these technologies can detect problematic content and proactively promote accurate scientific information. The analysis reveals patterns in how misinformation spreads through automated accounts, emotional triggers, and network effects. Beyond detection capabilities, AI can generate accessible scientific content, tailor communication to address public concerns, and personalize health messaging for diverse audiences. Despite promising applications, implementation faces challenges including distinguishing nuance from falsehood, addressing algorithmic bias, balancing free expression with harm prevention, ensuring system transparency, adapting to evolving tactics, and integrating human oversight effectively. Developing ethical AI solutions for health communication requires balancing technological capabilities with human expertise while safeguarding fundamental rights.
Keywords: Artificial Intelligence, bot detection, health misinformation, information ecosystems, sentiment analysis
Sentiment Analysis of Twitter Discourse on the 2023 Nigerian General Elections (Published)
Sentiment analysis entails discerning whether text conveys positive, neutral, or negative sentiments to ascertain the mood of the public concerning a given entity. This method relies on natural language processing, computational linguistics, and text analysis to identify, extract, and methodically analyze affective and subjective data. The 2023 Nigerian presidential election holds immense significance for the nation, determining its leadership for the subsequent four years. Consequently, comprehending public sentiment regarding the electoral process becomes paramount. This study sought to gauge public sentiment concerning the 2023 Nigerian General Elections by analyzing tweets related to candidates and their political parties. Leveraging three machine learning (ML) techniques—SVM, RF, and XGBoost—we aimed to categorize tweets as negative, positive, or neutral. Our dataset comprised a substantial volume of tweets, meticulously pre-processed to eliminate irrelevant content and noise. Results showcased the outstanding performance of RF and XGBoost in tweets classification and sentiment identification about the electoral process with the highest accuracy (93%) and precision (96%), occurring on neutral opinions. These results findings offer crucial insights into public opinion regarding candidates, their political parties, and the electoral procedure, benefiting researchers, political analysts and decision-makers alike. It suggests that 43% of the electorate expressed neutral sentiments about the elections, while 33% expressed positive sentiments, such as optimism about the electoral process, support for specific candidates, satisfaction with the results of the election, or excitement for taking part in democracy. Meanwhile, 24% of the electorate expressed negative sentiments, such as dissatisfaction with political candidates, criticism of the electoral processes, worries about fairness, or skepticism about the outcome. This research underscores the significance of sentiment analysis in comprehending public opinion and its potential contributions to political discourse.
Keywords: 2023 Nigerian general elections, sentiment analysis, twitter discourse