Identifying key success factors for startups With sentiment analysis using text data mining
It is aimed to identify the basic success factors, which are essential for startups as they intend to develop successful and profitable business models over time. To this end, it is attempted to analyze the sentiments on user-generated content (UGC) on Twitter. First, trigram word cloud is used. The...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2022-10-01
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| Series: | International Journal of Engineering Business Management |
| Online Access: | https://doi.org/10.1177/18479790221131612 |
| _version_ | 1811317050970210304 |
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| author | Tina Asgari Amir Daneshvar Adel Pourghader Chobar Maryam Ebrahimi Simon Abrahamyan |
| author_facet | Tina Asgari Amir Daneshvar Adel Pourghader Chobar Maryam Ebrahimi Simon Abrahamyan |
| author_sort | Tina Asgari |
| collection | DOAJ |
| description | It is aimed to identify the basic success factors, which are essential for startups as they intend to develop successful and profitable business models over time. To this end, it is attempted to analyze the sentiments on user-generated content (UGC) on Twitter. First, trigram word cloud is used. Then, a sentiment analysis is done with various predictive models including random forest, support-vector machine (SVM) and multilayer perceptron (MLP) to test the labeling of unlabeled data. To divide topics into negative, positive, and neutral sentiments, latent Dirichlet allocation (LDA) has been applied. According to the results, the MLP method on the basis of accuracy criterion offers an accuracy of 0.81, which is higher than other tested methods. In this regard, random forest and SVM methods provide accuracy of 0.78 and 0.80, respectively. Voting and stacking algorithms were used to increase the accuracy of the algorithms. However, it is found that with the use of voting method, the accuracy is almost equal to the results obtained from the MLP and with stacking method the accuracy is less than all three methods. Using word cloud, it is indicated that the most negative trigram is startups innovation regarding climate change, the most positive one is product marketing management and business-related concepts are determined as neutral. It is found that startup acceleration process, pushing for quicker completion, delivering the best product at the beginning of the project, poor management practices, and focusing just on properties are grouped as negative sentiments. On the other hand, sustainable and innovative business plan, the presence of experienced entrepreneurs and investors, coronavirus (COVID-19), and innovation are recognized as positive sentiments, and no analysis is given for neutral sentiments. |
| first_indexed | 2024-04-13T12:00:05Z |
| format | Article |
| id | doaj.art-2ee545bd9cfe42aa88cf65abbd0766f4 |
| institution | Directory Open Access Journal |
| issn | 1847-9790 |
| language | English |
| last_indexed | 2024-04-13T12:00:05Z |
| publishDate | 2022-10-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Engineering Business Management |
| spelling | doaj.art-2ee545bd9cfe42aa88cf65abbd0766f42022-12-22T02:47:48ZengSAGE PublishingInternational Journal of Engineering Business Management1847-97902022-10-011410.1177/18479790221131612Identifying key success factors for startups With sentiment analysis using text data miningTina AsgariAmir DaneshvarAdel Pourghader ChobarMaryam EbrahimiSimon AbrahamyanIt is aimed to identify the basic success factors, which are essential for startups as they intend to develop successful and profitable business models over time. To this end, it is attempted to analyze the sentiments on user-generated content (UGC) on Twitter. First, trigram word cloud is used. Then, a sentiment analysis is done with various predictive models including random forest, support-vector machine (SVM) and multilayer perceptron (MLP) to test the labeling of unlabeled data. To divide topics into negative, positive, and neutral sentiments, latent Dirichlet allocation (LDA) has been applied. According to the results, the MLP method on the basis of accuracy criterion offers an accuracy of 0.81, which is higher than other tested methods. In this regard, random forest and SVM methods provide accuracy of 0.78 and 0.80, respectively. Voting and stacking algorithms were used to increase the accuracy of the algorithms. However, it is found that with the use of voting method, the accuracy is almost equal to the results obtained from the MLP and with stacking method the accuracy is less than all three methods. Using word cloud, it is indicated that the most negative trigram is startups innovation regarding climate change, the most positive one is product marketing management and business-related concepts are determined as neutral. It is found that startup acceleration process, pushing for quicker completion, delivering the best product at the beginning of the project, poor management practices, and focusing just on properties are grouped as negative sentiments. On the other hand, sustainable and innovative business plan, the presence of experienced entrepreneurs and investors, coronavirus (COVID-19), and innovation are recognized as positive sentiments, and no analysis is given for neutral sentiments.https://doi.org/10.1177/18479790221131612 |
| spellingShingle | Tina Asgari Amir Daneshvar Adel Pourghader Chobar Maryam Ebrahimi Simon Abrahamyan Identifying key success factors for startups With sentiment analysis using text data mining International Journal of Engineering Business Management |
| title | Identifying key success factors for startups With sentiment analysis using text data mining |
| title_full | Identifying key success factors for startups With sentiment analysis using text data mining |
| title_fullStr | Identifying key success factors for startups With sentiment analysis using text data mining |
| title_full_unstemmed | Identifying key success factors for startups With sentiment analysis using text data mining |
| title_short | Identifying key success factors for startups With sentiment analysis using text data mining |
| title_sort | identifying key success factors for startups with sentiment analysis using text data mining |
| url | https://doi.org/10.1177/18479790221131612 |
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