Business Decision Support System based on Sentiment Analysis

Oppong, Stephen Opoku; Asamoah, Dominic; Oppong, Emmanuel Ofori; Lamptey, Derrick

doi:10.5815/ijieeb.2019.01.05

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…However, this continuously changing scenario of pandemics may not rely only in past history as the circumstances are rapidly changing. This article aligns with the line of works that argue that sentiment analysis can bring some light in business decisions for anticipating which products and services may be more successful in new circumstances (Oppong et al, 2019).…”

Section: Introductionsupporting

confidence: 68%

A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

Kumar

Nasralla

García‐Magariño

et al. 2021

PeerJ Computer Science

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The COVID-19 pandemic is changing daily routines for many citizens with a high impact on the economy in some sectors. Small-medium enterprises of some sectors need to be aware of both the pandemic evolution and the corresponding sentiments of customers in order to figure out which are the best commercialization techniques. This article proposes an expert system based on the combination of machine learning and sentiment analysis in order to support business decisions with data fusion through web scraping. The system uses human-centric artificial intelligence for automatically generating explanations. The expert system feeds from online content from different sources using a scraping module. It allows users to interact with the expert system providing feedback, and the system uses this feedback to improve its recommendations with supervised learning.

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Section: Introductionsupporting

confidence: 68%

A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

Kumar

Nasralla

García‐Magariño

et al. 2021

PeerJ Computer Science

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show abstract

“…SentDesk was an attempt by (Oppong et al 2019) to create a system that may aid businesses by taking user sentiment into account while making decisions. Some businesses in Ghana used this technique, and the results showed that it was superior to human evaluation.…”

Section: Related Workmentioning

confidence: 99%

A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets

Durga,

Sudhakar

2023

Multidiscip. Sci. J.

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Before making any decision, especially a financial one, people use social media to chat to, learn about, and obtain advice from others. How can social media opinions be relied on and utilised economically? Due to social media sites like Facebook and Twitter, where millions of individuals offer real-time thoughts on anything using slang and emoticons, data science researchers have access to more unstructured yet useful information. Social media includes Twitter. 326 million active social media users sent 500 million tweets daily in July 2018, indicating the sector's rapid expansion. Tweets can be viewed, edited, and sent. Knowing how to interpret tweets, which might be facts or views, is helpful. Such a study may predict the stock market, election, response event, news, and subjectivity scales. Tweets are analysed using Sentiment Analysis (SA). On the other hand, Sentiment analysis provides a broad overview of tweet polarity and is not beneficial for decision-making. In this research, we design a natural language processing (NLP) Twitter SA model to overcome the challenges of tweet sentiment recognition and build a hybrid system that can recognise and classify sentiment in Twitter's real-time reactions to any topic. We use a three-classifier machine learning method to analyse tweets from the Sentiment140 dataset (Logistic Regression, Bernoulli Naive Bayes, and SVM). Our findings are supported by the Term Frequency-Inverse Document Frequency analysis (TF-IDF). After that, accuracy and F1 Scores are used to evaluate these classifiers.

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“…The results of the survey confirm that government agencies, the Saudi example, can gain the power to implement the BPR successfully, especially when the BPR movement started on a small scale and is implemented with the knowledge management. The authors in [27], in their proposed approach, create three organizational marketers in order to annotate the emotions and also to mark the feelings of each instance of the corpus. The results of the evaluation, returned by the simulation, show that this approach works better than humans.…”

Section: Comparative Studymentioning

confidence: 99%

Towards an Intelligent Approach to Workflow Integration in a Quality Management System

OMRI¹,

AONNE²

2022

IJISA

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Among the most important activities within a company we find that of quality management. This activity represents reflects the most rigorous way possible for a better organization of establishments in order to offer the best service to customers and to the various members of these establishments. This activity of quality management is a very delicate and sensitive task due to the large number of documents and business processes that are handled on a cyclical basis. For this reason, setting up a reliable and efficient system for managing the different aspects of the quality management process becomes a challenge for any company that seeks excellence. This article proposes a new intelligent approach to the need of the management of human and commercial resources within the companies for a good management of the process of quality management according to its own conception. Our approach allows any quality management manager to manage the different modules of a QMS according to the ISO 9001 standard through the different interfaces offered by our solution. The monitoring phase of this process through the implementation of a workflow orchestrator, jBpm.

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Business Decision Support System based on Sentiment Analysis

Cited by 8 publications

References 18 publications

A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

A machine-learning scraping tool for data fusion in the analysis of sentiments about pandemics for supporting business decisions with human-centric AI explanations

A decision-making framework utilizing machine learning techniques, based on analysing the sentiment of tweets

Towards an Intelligent Approach to Workflow Integration in a Quality Management System

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