Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance
Abstract The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2022-09-01
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| Series: | Humanities & Social Sciences Communications |
| Online Access: | https://doi.org/10.1057/s41599-022-01325-y |
| _version_ | 1811202774656876544 |
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| author | Salman Sigari Amir. H. Gandomi |
| author_facet | Salman Sigari Amir. H. Gandomi |
| author_sort | Salman Sigari |
| collection | DOAJ |
| description | Abstract The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves. |
| first_indexed | 2024-04-12T02:45:19Z |
| format | Article |
| id | doaj.art-679e8cdac5524c6ab82392802a06e6a9 |
| institution | Directory Open Access Journal |
| issn | 2662-9992 |
| language | English |
| last_indexed | 2024-04-12T02:45:19Z |
| publishDate | 2022-09-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Humanities & Social Sciences Communications |
| spelling | doaj.art-679e8cdac5524c6ab82392802a06e6a92022-12-22T03:51:12ZengSpringer NatureHumanities & Social Sciences Communications2662-99922022-09-019111010.1057/s41599-022-01325-yAnalyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performanceSalman Sigari0Amir. H. Gandomi1Bloomberg L.PUniversity of Technology SydneyAbstract The complexity of business decision-making has increased over the years. It is essential for managers to gain a confident understanding of their business environments in order to make successful decisions. With the growth of opinion-rich web resources such as social media, discussion forums, review sites, news corpora, and blogs available on the internet, product and service reviews have become an essential source of information. In a data-driven world, they will improve services and operational insights to achieve real business benefits and help enterprises remain competitive. Despite the prevalence of textual data, few studies have demonstrated the effectiveness of real-time text mining and reporting tools in firms and organizations. To address this aspect of decision-making, we have developed and evaluated an unsupervised learning system to automatically extract and classify topics and their emotion score in text streams. Data were collected from commercial websites, open-access databases, and social networks to train the model. In the experiment, the polarity score was quantified at four different levels: word, sentence, paragraph, and the entire text using Latent Dirichlet Allocation (LDA). Using subjective data mining, we demonstrate how to extract, summarize, and track various aspects of information from the Web and help traditional information retrieval (IR) systems to capture more information. An opinion tracking system presented by our model extracts subjective information, classifies them, and tracks opinions by utilizing location, time, and reviewers’ positions. Using the online-offline data collection technique, we can update the library topic in real-time to provide users with a market opinion tracker. For marketing or economic research, this approach may be useful. In the experiment, the new model is applied to a case study to demonstrate how the business process improves.https://doi.org/10.1057/s41599-022-01325-y |
| spellingShingle | Salman Sigari Amir. H. Gandomi Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance Humanities & Social Sciences Communications |
| title | Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance |
| title_full | Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance |
| title_fullStr | Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance |
| title_full_unstemmed | Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance |
| title_short | Analyzing the past, improving the future: a multiscale opinion tracking model for optimizing business performance |
| title_sort | analyzing the past improving the future a multiscale opinion tracking model for optimizing business performance |
| url | https://doi.org/10.1057/s41599-022-01325-y |
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