Concept-level sentiment analysis in the manufacturing industry
The rapid evolution in the field of artificial intelligence (AI) have produced huge impacts on human society, business and industries. AI has been described as the key for the industrial revolution 4.0, in which many of the industrial companies has begun to adopt this powerful technology in pursuit...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138444 |
| _version_ | 1824456141563756544 |
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| author | Teng, He Xu |
| author2 | Erik Cambria |
| author_facet | Erik Cambria Teng, He Xu |
| author_sort | Teng, He Xu |
| collection | NTU |
| description | The rapid evolution in the field of artificial intelligence (AI) have produced huge impacts on human society, business and industries. AI has been described as the key for the industrial revolution 4.0, in which many of the industrial companies has begun to adopt this powerful technology in pursuit of efficiency and productivity. As one of the subfields of AI, sentiment analysis has been raising its attention in the industrial field. Sentiment analysis is the process of determining the opinion about a given subject from written or spoken language.
This project aims to make use of concept-level sentiment analysis to develop and build a sentiment analysis knowledge base that is specialized in the Advanced Manufacturing and Engineering industry (AME) under the sixth technology plan in Singapore – the Research Innovation Enterprise (RIE) plan 2020. It involves knowledge extraction from the AME industry domain, understands and process the meaning of domain specific terms and normally used terms by applying Nature Language Processing(NLP) techniques. Then, to design and develop a concept-level sentiment analysis system to perform polarity detection for the manufacturing industry.
The aim of performing sentiment analysis in the industry is to improve human-machine interaction. By filtering the massive amount of data generated from the manufacturing industry, we can identify keywords and tokens that are meaningful for decision making. Manufacturers can make use of this technology to improve quality control, standardization and maintenance through producing predictive analyzes of equipment functionality and radically streamlining factory lines.
Manufacturing will continue to be an important pillar of Singapore’s economy. Research & Development and technology such as AI, play key roles in strengthening existing manufacturing sectors , seeding new growth niches and boosting productivity. |
| first_indexed | 2025-02-19T03:49:23Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138444 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:49:23Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1384442020-05-06T05:21:34Z Concept-level sentiment analysis in the manufacturing industry Teng, He Xu Erik Cambria School of Computer Science and Engineering Centre for Computational Intelligence cambria@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence The rapid evolution in the field of artificial intelligence (AI) have produced huge impacts on human society, business and industries. AI has been described as the key for the industrial revolution 4.0, in which many of the industrial companies has begun to adopt this powerful technology in pursuit of efficiency and productivity. As one of the subfields of AI, sentiment analysis has been raising its attention in the industrial field. Sentiment analysis is the process of determining the opinion about a given subject from written or spoken language. This project aims to make use of concept-level sentiment analysis to develop and build a sentiment analysis knowledge base that is specialized in the Advanced Manufacturing and Engineering industry (AME) under the sixth technology plan in Singapore – the Research Innovation Enterprise (RIE) plan 2020. It involves knowledge extraction from the AME industry domain, understands and process the meaning of domain specific terms and normally used terms by applying Nature Language Processing(NLP) techniques. Then, to design and develop a concept-level sentiment analysis system to perform polarity detection for the manufacturing industry. The aim of performing sentiment analysis in the industry is to improve human-machine interaction. By filtering the massive amount of data generated from the manufacturing industry, we can identify keywords and tokens that are meaningful for decision making. Manufacturers can make use of this technology to improve quality control, standardization and maintenance through producing predictive analyzes of equipment functionality and radically streamlining factory lines. Manufacturing will continue to be an important pillar of Singapore’s economy. Research & Development and technology such as AI, play key roles in strengthening existing manufacturing sectors , seeding new growth niches and boosting productivity. Bachelor of Engineering (Computer Science) 2020-05-06T05:21:34Z 2020-05-06T05:21:34Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138444 en PSCSE18-0077 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Teng, He Xu Concept-level sentiment analysis in the manufacturing industry |
| title | Concept-level sentiment analysis in the manufacturing industry |
| title_full | Concept-level sentiment analysis in the manufacturing industry |
| title_fullStr | Concept-level sentiment analysis in the manufacturing industry |
| title_full_unstemmed | Concept-level sentiment analysis in the manufacturing industry |
| title_short | Concept-level sentiment analysis in the manufacturing industry |
| title_sort | concept level sentiment analysis in the manufacturing industry |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/138444 |
| work_keys_str_mv | AT tenghexu conceptlevelsentimentanalysisinthemanufacturingindustry |