Artificial intelligence and natural language processing (NLP) are increasingly being used in customer service to interact with users and answer their questions. The goal of this systematic review is to examine existing research on the use of NLP technology in customer service, including the research domain, applications, datasets used, and evaluation methods. The review also looks at the future direction of the field and any significant limitations. The review covers the time period from 2015 to 2022 and includes papers from five major scientific databases. Chatbots and question-answering systems were found to be used in 10 main fields, with the most common use in general, social networking, and e-commerce areas. Twitter was the second most commonly used dataset, with most research also using their own original datasets. Accuracy, precision, recall, and F1 were the most common evaluation methods. Future work aims to improve the performance and understanding of user behavior and emotions, and address limitations such as the volume, diversity, and quality of datasets. This review includes research on different spoken languages and models and techniques.

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

In this chapter, we review and discuss the transformation of AI technology in HCI/UX work and assess how AI technology will change how we do the work. We first discuss how AI can be used to enhance the result of user research and design evaluation. We then discuss how AI technology can be used to enhance HCI/UX design. Finally, we discuss how AI-enabled capabilities can improve UX when users interact with computing systems, applications, and services.

问答系统被认为是流行且经常有效的信息在网络上寻求信息的手段。在这样的系统中，寻求信息者可以通过自然语言提出问题来获得对他们的查询的简短回应。交互式问题回答是一种最近提出且日益流行的解决方案，它位于问答和对话系统的交集。一方面，用户可以以普通语言提出问题，并找到对她的询问的实际回答；另一方面，如果在初始请求中有多个可能的答复，很少或歧义，则系统可以将问题交通会话延长到对话中。通过允许用户提出更多问题，交互式问题回答使用户能够与系统动态互动并获得更精确的结果。这项调查提供了有关当前文献中普遍存在的交互式提问方法的详细概述。它首先要解释提问系统的基本原理，从而定义新的符号和分类法，以将所有已确定的作品结合在统一框架内。然后，根据提出的方法，评估方法和数据集/应用程序域来介绍和检查有关交互式问题解答系统的审查已发表的工作。我们还描述了围绕社区提出的特定任务和问题的趋势，从而阐明了学者的未来利益。 GitHub页面的综合综合了本文献研究中涵盖的所有主要主题，我们的工作得到了进一步的支持。 https://sisinflab.github.io/interactive-question-answering-systems-survey/

发现别人认为是我们信息收集策略的关键方面。现在，人们可以积极利用信息技术来寻找和理解他人的想法，这要归功于越来越多的意见资源（例如在线评论网站和个人博客）的越来越多。由于其在理解人们的意见方面的关键功能，因此情感分析（SA）是一项至关重要的任务。另一方面，现有的研究主要集中在英语上，只有少量研究专门研究低资源语言。对于情感分析，这项工作根据用户评估提供了一个新的多级乌尔都语数据集。高音扬声器网站用于获取乌尔都语数据集。我们提出的数据集包括10,000项评论，这些评论已被人类专家精心归类为两类：正面，负面。这项研究的主要目的是构建一个手动注释的数据集进行乌尔都语情绪分析，并确定基线结果。采用了五种不同的词典和规则的算法，包括NaiveBayes，Stanza，TextBlob，Vader和Flair，实验结果表明，其精度为70％的天赋优于其他经过测试的算法。

This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highlighting the operations of the action modules, likely the Retrieval system, Question-Answering, and Recommender system. We labeled various ConvSearch research problems in knowledge bases, natural language processing, and dialogue management systems along with the action modules. We further categorized the framework to ConvSearch and the application is directed toward biomedical and healthcare fields for the utilization of clinical social technology. Finally, we conclude by talking through the challenges and issues of ConvSearch, particularly in Bio-Medicine. Our main aim is to provide an integrated and unified vision of the ConvSearch components from different fields, which benefit the information-seeking process in healthcare systems.

Chatbots已经彻底改变了人类与计算机系统互动的方式，他们替代了服务代理，呼叫中心代表等。健身行业一直是一种不断增长的行业，尽管它还没有适应AI，ML和云计算等最新技术。在本文中，我们建议使用IBM Watson开发适合健身管理的聊天栏，并将其与Web应用程序集成。我们建议使用自然语言处理（NLP）和自然语言理解（NLU）以及为Chatbot Assistant提供的IBM Cloud Watson框架。该软件采用无服务器架构，通过提供饮食计划，家庭练习，互动咨询会，健身建议，将专业的服务结合起来。

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.

Customers are rapidly turning to social media for customer support. While brand agents on these platforms are motivated and well-intentioned to help and engage with customers, their efforts are often ignored if their initial response to the customer does not match a specific tone, style, or topic the customer is aiming to receive. The length of a conversation can reflect the effort and quality of the initial response made by a brand toward collaborating and helping consumers, even when the overall sentiment of the conversation might not be very positive. Thus, through this study, we aim to bridge this critical gap in the existing literature by analyzing language's content and stylistic aspects such as expressed empathy, psycho-linguistic features, dialogue tags, and metrics for quantifying personalization of the utterances that can influence the engagement of an interaction. This paper demonstrates that we can predict engagement using initial customer and brand posts.

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.

通过为患者启用远程医疗服务，远程医疗有助于促进医疗专业人员的机会。随着必要的技术基础设施的出现，这些服务已逐渐流行。自从Covid-19危机开始以来，远程医疗的好处就变得更加明显，因为人们在大流行期间倾向于亲自探望医生。在本文中，我们专注于促进医生和患者之间的聊天课程。我们注意到，随着对远程医疗服务的需求的增加，聊天体验的质量和效率可能至关重要。因此，我们为医学对话开发了一种智能的自动反应生成机制，该机制可帮助医生有效地对咨询请求做出反应，尤其是在繁忙的课程中。我们探索了9个月内收集的医生和患者之间的900,000多个匿名的历史在线信息。我们实施聚类算法，以确定医生最常见的响应，并相应地手动标记数据。然后，我们使用此预处理数据来训练机器学习算法以生成响应。所考虑的算法有两个步骤：过滤（即触发）模型，以滤除不可行的患者消息和一个响应发生器，以建议成功通过触发阶段的响应前3位医生响应。该方法为Precision@3提供了83.28 \％的精度，并显示出其参数的鲁棒性。

在科学研究中，该方法是解决科学问题和关键研究对象的必不可少手段。随着科学的发展，正在提出，修改和使用许多科学方法。作者在抽象和身体文本中描述了该方法的详细信息，并且反映该方法名称的学术文献中的关键实体称为方法实体。在大量的学术文献中探索各种方法实体有助于学者了解现有方法，为研究任务选择适当的方法并提出新方法。此外，方法实体的演变可以揭示纪律的发展并促进知识发现。因此，本文对方法论和经验作品进行了系统的综述，重点是从全文学术文献中提取方法实体，并努力使用这些提取的方法实体来建立知识服务。首先提出了本综述涉及的关键概念的定义。基于这些定义，我们系统地审查了提取和评估方法实体的方法和指标，重点是每种方法的利弊。我们还调查了如何使用提取的方法实体来构建新应用程序。最后，讨论了现有作品的限制以及潜在的下一步。

自然语言处理（NLP）是一个人工智能领域，它应用信息技术来处理人类语言，在一定程度上理解并在各种应用中使用它。在过去的几年中，该领域已经迅速发展，现在采用了深层神经网络的现代变体来从大型文本语料库中提取相关模式。这项工作的主要目的是调查NLP在药理学领域的最新使用。正如我们的工作所表明的那样，NLP是药理学高度相关的信息提取和处理方法。它已被广泛使用，从智能搜索到成千上万的医疗文件到在社交媒体中找到对抗性药物相互作用的痕迹。我们将覆盖范围分为五个类别，以调查现代NLP方法论，常见的任务，相关的文本数据，知识库和有用的编程库。我们将这五个类别分为适当的子类别，描述其主要属性和想法，并以表格形式进行总结。最终的调查介绍了该领域的全面概述，对从业者和感兴趣的观察者有用。

Drawing from the resources of psychoanalysis and critical media studies, in this paper we develop an analysis of Large Language Models (LLMs) as automated subjects. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which AI behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise state-of-the-art natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be helpful, truthful and harmless by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer, its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.

Sentiment analysis or opinion mining help to illustrate the phrase NLP (Natural Language Processing). Sentiment analysis has been the most significant topic in recent years. The goal of this study is to solve the sentiment polarity classification challenges in sentiment analysis. A broad technique for categorizing sentiment opposition is presented, along with comprehensive process explanations. With the results of the analysis, both sentence-level classification and review-level categorization are conducted. Finally, we discuss our plans for future sentiment analysis research.

本次调查绘制了用于分析社交媒体数据的生成方法的研究状态的广泛的全景照片（Sota）。它填补了空白，因为现有的调查文章在其范围内或被约会。我们包括两个重要方面，目前正在挖掘和建模社交媒体的重要性：动态和网络。社会动态对于了解影响影响或疾病的传播，友谊的形成，友谊的形成等，另一方面，可以捕获各种复杂关系，提供额外的洞察力和识别否则将不会被注意的重要模式。

在线发布的产品评论数量越来越多的是设计师的金矿，通过捕捉客户的声音，并相应地改善这些产品，了解他们开发的产品。与此同时，产品设计和开发在创造更可持续的未来方面具有重要作用。随着自然语言处理领域的人工智能技术最近，该研究旨在开发一体化机器学习解决方案，以便自动从线产品评论获得可持续设计的洞察。在本文中，讨论了，说明了现有框架 - 包括Python库，软件包以及伯爵等最先进的算法的机会和挑战。这一贡献讨论了达成的机会和建立机器学习管道的挑战，以便从产品审查中获取有限性，以设计更可持续的产品，包括五个阶段，包括与解释的可持续性相关的审查可持续设计引导：数据收集，数据格式，模型培训，模型评估和模型部署。给出了可持续设计见解的例子，可提供退出产品审查采矿和加工。最后，提供了用于该领域未来研究的有希望的线，包括与可持续替代品的平行标准产品的案例研究，以比较客户价值的特征，并在优质的可持续设计引线中产生。

通过分析大量数据来提供决策支持，大数据正在改革许多工业域。大数据测试旨在确保大数据系统在维护数据的性能和质量时运行平稳且无错误。但是，由于数据的多样性和复杂性，测试大数据具有挑战性。虽然众多研究对大数据测试进行了综合审查，但解决了测试技术和挑战的综合性尚未混淆。因此，我们对大数据测试技术（2010年 - 2021年）进行了系统审查。本文通过突出显示每个处理阶段的技术来讨论测试数据的处理。此外，我们讨论了挑战和未来的方向。我们的发现表明，已经使用不同的功能，非功能性和组合（功能和非功能性）测试技术来解决与大数据相关的特定问题。同时，在MapReduce验证阶段，大多数测试挑战都面临。此外，组合测试技术是与其他技术相结合的应用技术之一（即随机测试，突变测试，输入空间分区和等价测试），以解决在大数据测试期间面临的各种功能故障挑战。

在这个大数据时代，当前一代很难从在线平台中包含的大量数据中找到正确的数据。在这种情况下，需要一个信息过滤系统，可以帮助他们找到所需的信息。近年来，出现了一个称为推荐系统的研究领域。推荐人变得重要，因为他们拥有许多现实生活应用。本文回顾了推荐系统在电子商务，电子商务，电子资源，电子政务，电子学习和电子生活中的不同技术和发展。通过分析有关该主题的最新工作，我们将能够详细概述当前的发展，并确定建议系统中的现有困难。最终结果为从业者和研究人员提供了对建议系统及其应用的必要指导和见解。

社交媒体有可能提供有关紧急情况和突然事件的及时信息。但是，在每天发布的数百万帖子中找到相关信息可能很困难，并且开发数据分析项目通常需要时间和技术技能。这项研究提出了一种为分析社交媒体的灵活支持的方法，尤其是在紧急情况下。引入了可以采用社交媒体分析的不同用例，并讨论了从大量帖子中检索信息的挑战。重点是分析社交媒体帖子中包含的图像和文本，以及一组自动数据处理工具，用于过滤，分类和使用人类的方法来支持数据分析师的内容。这种支持包括配置自动化工具的反馈和建议，以及众包收集公民的投入。通过讨论Crowd4SDG H2020欧洲项目中开发的三个案例研究来验证结果。