Multi-Purpose NLP Chatbot : Design, Methodology & Conclusion

With a major focus on its history, difficulties, and promise, this research paper provides a thorough analysis of the chatbot technology environment as it exists today. It provides a very flexible chatbot system that makes use of reinforcement learning strategies to improve user interactions and conversational experiences. Additionally, this system makes use of sentiment analysis and natural language processing to determine user moods. The chatbot is a valuable tool across many fields thanks to its amazing characteristics, which include voice-to-voice conversation, multilingual support [12], advising skills, offline functioning, and quick help features. The complexity of chatbot technology development is also explored in this study, along with the causes that have propelled these developments and their far-reaching effects on a range of sectors. According to the study, three crucial elements are crucial: 1) Even without explicit profile information, the chatbot system is built to adeptly understand unique consumer preferences and fluctuating satisfaction levels. With the use of this capacity, user interactions are made to meet their wants and preferences. 2) Using a complex method that interlaces Multiview voice chat information, the chatbot may precisely simulate users' actual experiences. This aids in developing more genuine and interesting discussions. 3) The study presents an original method for improving the black-box deep learning models' capacity for prediction. This improvement is made possible by introducing dynamic satisfaction measurements that are theory-driven, which leads to more precise forecasts of consumer reaction.Comment: Multilingual , Voice Conversion , Emotion Recognition , Offline Service , Financial Advisor , Product Preference , Customer Reaction Predictio

Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty

AI, ML, and NLP are profoundly altering the way organizations work. With the increasing influx of data and the development of AI systems to understand it in order to solve business challenges, the excitement surrounding AI has grown. Massive datasets, computer capacity, improved algorithms, accessible algorithm libraries, and frameworks have compelled today's organizations to use AI to enhance their operations and profits. These technologies aid every kind of industry, from agriculture to finance. More specifically, AI and ML, and NLP are assisting organizations in areas such as customer service, predictive modeling, customer personalization, picture identification, sentiment analysis, offline and online document processing. The purpose of this study was twofold. We first review the several applications of AI in business and then empirically test whether these applications increase customer loyalty using the datasets of 910 firms around the world.  The datasets include the integration scores of four different AI features, namely, AI-powered customer service, predictive modeling, ML-powered personalization, and natural language processing integration. The target is the customer loyalty measure as binary. All the features are measured on a 5-pint Likert scale. We applied six different supervised machine learning algorithms, namely, Logistic regression, KNN, SVM, Decision Tree, Random Forest, and Ada boost Classifiers. the performance of each algorithm was evaluated using confusion matrices and ROC curves. The Ada boost and logistic classifiers performed better with test accuracies of 0.639 and 0.631, respectively. The decision tree and KNN had the performance with accuracies of 0.532 and 0.570, respectively.  The findings of this study highlight that by incorporating AI, ML, and NLP, businesses may analyze data to uncover what's useful, gaining valuable insights that can be used to automate processes and drive business strategies. As a result, firms that wish to remain competitive and increase customer loyalty should adopt them

Detecting and Classifying Malevolent Dialogue Responses: Taxonomy, Data and Methodology

Conversational interfaces are increasingly popular as a way of connecting people to information. Corpus-based conversational interfaces are able to generate more diverse and natural responses than template-based or retrieval-based agents. With their increased generative capacity of corpusbased conversational agents comes the need to classify and filter out malevolent responses that are inappropriate in terms of content and dialogue acts. Previous studies on the topic of recognizing and classifying inappropriate content are mostly focused on a certain category of malevolence or on single sentences instead of an entire dialogue. In this paper, we define the task of Malevolent Dialogue Response Detection and Classification (MDRDC). We make three contributions to advance research on this task. First, we present a Hierarchical Malevolent Dialogue Taxonomy (HMDT). Second, we create a labelled multi-turn dialogue dataset and formulate the MDRDC task as a hierarchical classification task over this taxonomy. Third, we apply stateof-the-art text classification methods to the MDRDC task and report on extensive experiments aimed at assessing the performance of these approaches.Comment: under review at JASIS

Investigating the Impacts of AR, AI, and Website Optimization on Ecommerce Sales Growth

E-commerce has evolved into a vital element of modern life by giving customers a quick and easy way to buy products and services online. Businesses increasingly focus on building their online presence in order to remain competitive, which represents a huge change as a result of the growth of e-commerce. Utilizing artificial intelligence (AI), augmented reality (AR), and website optimization is one of the primary ways firms are aiming to improve their e-commerce operations at the moment. While AR can improve product recommendations and the visual component of online shopping by giving customers a more immersive experience, AI can be used to tailor the user experience and boost personalization. On the other side, website optimization can assist companies in enhancing the user experience and raising conversion rates. Businesses can make better choices about how to implement these variables into their operations by knowing how they affect e-commerce sales. This study used data from 190 global e-commerce sites to empirically examine the effects of using AI, AR, and website optimization on the increase of e-commerce sales. The study used a multiple regression analysis to look at how these factors and the rise of e-commerce relate to one another. The study's findings demonstrated that every element had a favorable and significant impact on the increase of e-commerce sales. This suggests that companies investing in artificial intelligence, augmented reality, and website optimization can anticipate a comparable rise in revenue. These results suggest that companies wishing to enhance their e-commerce operations should think about investing in AI, AR, and website optimization. They may improve client satisfaction this way, boost conversion rates, and eventually boost sales. &nbsp

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Overcoming Racial Harms to Democracy from Artificial Intelligence

While the United States is becoming more racially diverse, generative artificial intelligence and related technologies threaten to undermine truly representative democracy. Left unchecked, AI will exacerbate already substantial existing challenges, such as racial polarization, cultural anxiety, antidemocratic attitudes, racial vote dilution, and voter suppression. Synthetic video and audio (“deepfakes”) receive the bulk of popular attention—but are just the tip of the iceberg. Microtargeting of racially tailored disinformation, racial bias in automated election administration, discriminatory voting restrictions, racially targeted cyberattacks, and AI-powered surveillance that chills racial justice claims are just a few examples of how AI is threatening democracy. Unfortunately, existing laws—including the Voting Rights Act—are unlikely to address the challenges. These problems, however, are not insurmountable if policymakers, activists, and technology companies act now. This Article asserts that AI should be regulated to facilitate a racially inclusive democracy, proposes novel principles that provide a framework to regulate AI, and offers specific policy interventions to illustrate the implementation of the principles. Even though race is the most significant demographic factor that shapes voting patterns in the United States, this is the first article to comprehensively identify the racial harms to democracy posed by AI and offer a way forward

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

SENTIMENT AND BEHAVIORAL ANALYSIS IN EDISCOVERY

A suspect or person-of-interest during legal case review or forensic evidence review can exhibit signs of their individual personality through the digital evidence collected for the case. Such personality traits of interest can be analytically harvested for case investigators or case reviewers. However, manual review of evidence for such flags can take time and contribute to increased costs. This study focuses on certain use-case scenarios of behavior and sentiment analysis as a critical requirement for a legal case’s success. This study aims to quicken the review and analysis phase and offers a software prototype as a proof-of-concept. The study starts with the build and storage of Electronic Stored Information (ESI) datasets for three separate fictitious legal cases using publicly available data such as emails, Facebook posts, tweets, text messages and a few custom MS Word documents. The next step of this study leverages statistical algorithms and automation to propose approaches towards identifying human sentiments, behavior such as, evidence of financial fraud behavior, and evidence of sexual harassment behavior of a suspect or person-of-interest from the case ESI. The last stage of the study automates these approaches via a custom software and presents a user interface for eDiscovery teams and digital forensic investigators