Integrating Chatbot Application with Qlik Sense Business Intelligence (BI) Tool Using Natural Language Processing (NLP)

Vashisht, Vipul; Dharia, Pankaj

doi:10.1007/978-981-15-2329-8_69

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…The work by Vashisht and Dharia (2020) showed that the introduction of the chatbot and its connection with a BI tool had shown promising results. Liu et al (2020a) presented the most optimal method as a convolutional neural network based on sentence modeling in NLP tasks.…”

Section: Literature Surveymentioning

confidence: 99%

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Kumar¹,

Shidaganti²,

Anand³

et al. 2023

Journal of Computer Science

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Several product companies have turned to social media to analyze customer satisfaction and provide customer service to disgruntled customers. Product twitter handles are flooded with tweets every day. Customer service professionals struggle to find and resolve complaints from numerous tweets, which results in high wait times for a response, huge costs for the company, and frustrated customers. Automating customer services involves the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to emulate customer service offered by professionals. Pre-processing and deriving insights from real data are difficult and several start-ups cannot afford funds to maintain a data analysis team. Existing technologies for chatbots and analysis can be improved with new machine learning models, training existing models more and ensuring that responses generated resemble human interactions. BERT-CNN-BiLSTM modules were integrated into a model for sentiment analysis on scraped tweets from twitter using TWINT that achieved an accuracy of 96%. For the labeling and categorization of tweets, the logistic regression linear model achieved the highest accuracy of 97% compared to the other 3 classification models. For the automated chatbot, a model trained with BERT and open AI-GPT achieved an accuracy of 78%.

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Section: Literature Surveymentioning

confidence: 99%

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Kumar¹,

Shidaganti²,

Anand³

et al. 2023

Journal of Computer Science

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show abstract

“…To further complicate things, the form of Arabic letter is totally different than English language and vowels are omitted from written Arabic. Globally, there are 422 million Arabic speakers around the world [ 11 ]. According to a British council report, Arabic language took the second place for each native speaker who lived in northern Africa such as Algeria and Morocco and western Asia such as Georgia and Azerbaijan and took the first place at Arabian Peninsula such as Saudi Arabia and United Arab Emirates.…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Arabic Dialect Chatbot Using Machine Learning

Alhassan

Albarrak

Bhatia

et al. 2022

Computational Intelligence and Neuroscience

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With the advent of artificial intelligence and proliferation in the demand for an online dialogue system, the popularity of chatbots is growing on various industrial platforms. Their applications are getting widely noticed with intelligent tools as they are able to mimic human behavior in natural languages. Chatbots have been proven successful for many languages, such as English, Spanish, and French, over the years in varied fields like entertainment, medicine, education, and commerce. However, Arabic chatbots are challenging and are scarce, especially in the maintenance domain. Therefore, this research proposes a novel framework for an Arabic troubleshooting chatbot aiming at diagnosing and solving technical issues. The framework addresses the difficulty of using the Arabic language and the shortage of Arabic chatbot content. This research presents a realistic implementation of creating an Arabic corpus for the chatbot using the developed framework. The corpus is developed by extracting IT problems/solutions from multiple domains and reliable sources. The implementation is carried forward towards solving specific technical solutions from customer support websites taken from different well-known organizations such as Samsung, HP, and Microsoft. The claims are proved by evaluating and conducting experiments on the dataset by comparing with the previous researches done in this field using different metrics. Further, the validations are well presented by the proposed system that outperforms the previously developed different types of chatbots in terms of several parameters such as accuracy, response time, dataset data, and solutions given as per the user input.

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“…They allow decision-makers to interact intuitively with the DW by asking questions as they come to mind and without in-depth knowledge of the query tool or formal languages (SQL, MDX). However, researchers move recently towards integrating chatbots applications based on a NL Dialogue flow [45] in order to interact with dashboards. These applications enable decisionmakers to ask NL queries and receive instant responses instead of navigating in the dashboard.…”

Section: Introductionmentioning

confidence: 99%

“…These applications enable decisionmakers to ask NL queries and receive instant responses instead of navigating in the dashboard. This approach has good properties such as speed, accessibility, compatibility, and interactivity over traditional BI dashboard [45].…”

Section: Introductionmentioning

confidence: 99%

Natural language why-question in Business Intelligence applications: model and recommendation approach

et al. 2022

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Business Intelligence is the key technology for users to effectively extract valuable information from oceans of data for decision-making. Data warehouses and on-line analytical processing systems have therefore been developed to contribute effectively to the decision-making process. To extract information that is useful to decision-making, decision-makers express their needs in natural language. Such requirements may be formulated in natural language interfaces in free syntax, avoiding unfamiliar language (SQL, MDX). Natural Language queries can stand for WH-questions ("What, Who, Where, Why, etc.") or a set of Keywords. In this paper, we emphasize on the "Why-Question". This type of question provides answers that help in the diagnosis analysis of the data Warehouse. To deal with a Why-Question, we propose a model that mainly captures the components that reflect the multidimensional aspect of the Data Warehouse. When a decision-maker formulates his Why-Question in natural language, he uses his own terms. This decisional need can be not precise because the decision-maker is not always aware of the Data Warehouse's lexicon as well as the Why-Question's model. Consequently, the decision-maker must reformulate his initial question. Otherwise, the Why-Question's answering process will not be triggered. This situation is not obvious for a decision-maker, especially when the reformulation of the question becomes iterative. To handle these issues, we lean towards a Why-Question's recommendation approach based on both the Data Warehouse's content and the decision maker's requirement. This proposal aims to recommend to the decision-maker a set of natural language Why-Questions instead of rephrasing his initial question. To guide the recommendation process, we rely on a grammar that formalizes the Why-Question's model. To validate our approach, a tool called "WQ-Recommender" is developed. An experimental study is presented to evaluate the relevance of the proposal.

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Integrating Chatbot Application with Qlik Sense Business Intelligence (BI) Tool Using Natural Language Processing (NLP)

Cited by 10 publications

References 5 publications

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

Analyzing and Automating Customer Service Queries on Twitter Using Robotic Process Automation

A Novel Framework for Arabic Dialect Chatbot Using Machine Learning

Natural language why-question in Business Intelligence applications: model and recommendation approach

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