Chatbot or Chat-Blocker: Predicting Chatbot Popularity before Deployment
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Chatbots are widely employed in various scenarios. However, given the high costs of chatbot development and chatbots' tremendous social influence, chatbot failures may inevitably lead to a huge economic loss. Previous chatbot evaluation frameworks rely heavily on human evaluation, lending little support for automatic early-stage chatbot examination prior to deployment. To reduce the risk of potential loss, we propose a computational approach to extracting features and training models that make a priori prediction about chatbots' popularity, which indicates chatbot general performance. The features we extract cover chatbot Intent, Conversation Flow, and Response Design. We studied 1050 customer service chatbots on one of the most popular chatbot service platforms. Our model achieves 77.36% prediction accuracy among very popular and very unpopular chatbots, making the first step towards computational feedback before chatbot deployment. Our evaluation results also reveal the key design features associated with chatbot popularity and offer guidance on chatbot design.Keywords:
Chatbot
Popularity
A chatbot is a computer program that can understand the human language and respond like humans. The more it acts like a human, the more helpful the chatbot is. There are several attempts to make the chatbot intelligent or human-like. In college, the routine task is to answer the questions that have been asked repeatedly or found on the official website. This task is time-consuming and a wastes task. The chatbot is one of the solutions to this problem. A good chatbot can naturally answer such questions quickly and tirelessly. However, producing an intelligent or human-like chatbot is a challenge. The chatbot should be able to answer the fundamental questions in the domain and the advanced opinion questions. In this research, we proposed the chatbot system using the machine learning technique. Therefore, the chatbot can learn from the user to improve its performance. We evaluate our chatbot by testing with real-world conversation. The result shows that our chatbot can respond to the fundamental questions with a higher accuracy rate than the advanced questions.
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Today's widespread use of smartphones is proof that technology is always evolving. Nowadays, artificial intelligence is crucial to numerous industries, including manufacturing, human resources, and customer service. There are numerous chatbots that help people discover solutions to their questions. As a result, we are developing an AI-powered chatbot that can address all questions about colleges. It serves as an intelligence tool with an emphasis on higher education. This artificially intelligent machine will respond to queries from users regarding matters related to higher education. The information is kept in the chatbot's database so that it can be used to spot trends and decide how to respond to questions. The chatbot for college inquiries was developed using an NLP system that evaluates questions and comprehends messages. People react to each other differently based on emotions and attitude. Because chatbots must go by rules just like humans do, they will communicate with customers in a courteous and correct manner. Students can ask the chatbot any questions at any time of the day or night, and they will receive a prompt and accurate response. A chatbot can respond to thousands of users simultaneously. Chatbot can work 24 ×7 without getting tired. It has minimal errors, which increases productivity.
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The Artificial Intelligence (AI) chatbot is becoming more popular and has turned out to be one of the most prominent technologies for user assistance. AI chatbots are used to improve the customer experience by providing automated conversations, minimizing cost, and being capable of providing support in multiple languages. This research study has proposed a multi-linguistic health awareness chatbot for providing assistance to people. This AI chatbot will help people to get their queries answered related to diseases, treatment, and precautions. The advantage of this chatbot is that it is capable of providing information to users in their native language. This multi-linguistic chatbot is based on Natural Language Processing (NLP) model and built using the RASA SDK. To evaluate the proposed chatbot's performance, the chatbot's deployment is done using the RASA-X, which is the Conversation-Driven Development environment.
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ChatBot can be described as software that can chat with people using artificial intelligence. A chatbot is a PC program that reenacts human discussion through voice orders or text visits or both. Chatbot, short for chatter-bot, is a man-made reasoning (AI) include that can be implanted and utilized through any significant informing applications. This software are recycled to achieve tasks such as rapidly responding to consumers, informing them, assisting to purchase products and providing better service to customers. This research paper showing how a chatbot can be created through demonstration as well as their applications of chatbots in numerous areas such as traveling, banking, health, customer call centers and e-commerce. Additionally, the results of an example chatbbot for online food order service developed in the e-commerce domain is presented using Dialog-flow engine and Facebook Messanger. This research work represents the Chat flow, components of a chatbot, Implementation using Dialogflow.
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With the spreading of Marxism popularity in recent years,profound research has been done to the popularity of Marxism from different aspects and perspectives,including the popularity’s scientific implication,necessity,problems,past experiences,and the ways of promoting the popularity.The research is advantageous to the better development of Marxism popularity.
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Conversational interfaces allow users to experience artificial intelligence (AI) services through text or voice conversations. One common form of a conversational interface is a chatbot, which can be scenario-based or large language model (LLM)-based. A scenario-based chatbot generates a response within a predefined scenario for a user query on a specific domain or topic. The chatbot's response for recommendation is processed in conjunction with a separate algorithm. A LLM-based chatbot generates a response through a pretrained model to a user query on a wide range of topics. In this process, the LLM-based chatbot's response takes the form of a kind of recommendation, which is different from the existing recommendation services. To look at the issue more comprehensively, this paper examines recommendation-style system responses of a LLM-based chatbot with the principles of AI ethics. Several examples are shown where the chatbot's responses are modified according to principles of AI ethics.
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Chatbots are widely employed in various scenarios. However, given the high costs of chatbot development and chatbots' tremendous social influence, chatbot failures may inevitably lead to a huge economic loss. Previous chatbot evaluation frameworks rely heavily on human evaluation, lending little support for automatic early-stage chatbot examination prior to deployment. To reduce the risk of potential loss, we propose a computational approach to extracting features and training models that make a priori prediction about chatbots' popularity, which indicates chatbot general performance. The features we extract cover chatbot Intent, Conversation Flow, and Response Design. We studied 1050 customer service chatbots on one of the most popular chatbot service platforms. Our model achieves 77.36% prediction accuracy among very popular and very unpopular chatbots, making the first step towards computational feedback before chatbot deployment. Our evaluation results also reveal the key design features associated with chatbot popularity and offer guidance on chatbot design.
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Popularity
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The purpose of this paper is to explore the usefulness of chatbot in educational institutes such as schools and colleges and to propose a chatbot development plan that meet the needs. Usually chatbots are built for one specific purpose, for example, to answer general queries prospective students might have regarding admission. This paper aims to provide an artificial-intelligence(AI) integrated chatbot framework that can help develop a multi-use chatbot. Study is based highly on qualitative data collected from case studies and journal articles. Primary data is also collected from interviews and questionnaires presented to appropriate staffs and students in college, in this case, Middle East College. Integrating AI into the chatbot to make it self-reliant, intelligent and learn from user interaction is necessary to make it deal with multiple fields. This requires complex algorithms, database management and extensive labor, thus making it very costly. However, if developed, this single chatbot can help students, faculties and other staffs greatly, not just as an assistant in answering frequently asked questions, but also in learning and teaching. The chatbot can be integrated with mobile app making it a part of daily life. Due to over complexity, the chatbot will first developed for use in one field then gradually expanded to other. A chatbot built for multiple purposes certainly holds more complexity than a single general purpose chatbot. This being said, having the software developed and tested in real life would have helped to better understand its flexibility and functionality.
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This study aimed to create a chatbot for Korean language education using a chatbot builder and to suggest a chatbot utilization method for teaching and learning Korean. To this end, we developed a conversational artificial intelligence chatbot using Dialogflow—a chatbot builder provided by Google—and demonstrated how teachers can use the builders to create chatbots for Korean language education. The chatbots were developed by dividing them into for pattern practice, textbook dialogue practice, speech practice, and game activities, according to the purpose of use; furthermore, the utilization method of each chatbot was summarized. These discussions will contribute to establishing chatbots as useful educational aids in the current situation, with the increased development of artificial intelligence technology and the need for a distance education environment. Additionally, chatbot production and utilization through chatbot builders are expected to enable instructors to easily construct chatbots that account for the classroom environment and learners' characteristics. This in turn enables customized learning for individual learners; further, this is expected to play a positive role in learners' motivation and attitudes.
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