Auto capture on drug text detection in social media through NLP from the heterogeneous data
Auto Capture Drug Text detects (ACDTD) that textual content has currently gained important activities in Pharmaceutical drugs research. The recent studies focused on the different resources of textual content-related messages including social networks. Where a huge quantity of consumers posted infor...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-12-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917422001842 |
| _version_ | 1798023017832382464 |
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| author | P.M. Lavanya E. Sasikala |
| author_facet | P.M. Lavanya E. Sasikala |
| author_sort | P.M. Lavanya |
| collection | DOAJ |
| description | Auto Capture Drug Text detects (ACDTD) that textual content has currently gained important activities in Pharmaceutical drugs research. The recent studies focused on the different resources of textual content-related messages including social networks. Where a huge quantity of consumers posted information about the usage of pharmaceutical drugs and it's combined and cleared neatly. Our textual content classification strategies depend on regenerating the features of a large set of designs; it represents the semantic rules from the short textual nuggets. Significantly use the detailed structured files and it is combined by instruction data from the various collections to regulate the classification approach. Social Media Online Natural Language Processing (SMONLP) with ACDTD class achieves the F-scores of 0.90, 0.738, and 0.878 for the three information files. Integrating the instruction messages from various compatible files enhances the ACDTD F-scores for the in-house information files to 0.797 and 0.854 respectively. The heterogeneous textual content training using the cases where information sets are maintained may reduce the cost and time in the future. |
| first_indexed | 2024-04-11T17:40:35Z |
| format | Article |
| id | doaj.art-ef7fb518fb3e4e57ab13607ad4bffc4e |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-11T17:40:35Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-ef7fb518fb3e4e57ab13607ad4bffc4e2022-12-22T04:11:31ZengElsevierMeasurement: Sensors2665-91742022-12-0124100550Auto capture on drug text detection in social media through NLP from the heterogeneous dataP.M. Lavanya0E. Sasikala1Department of Computer Science and Engineering, SRM Institute of Science & Technology, Kattankulathur, India; Department of Information Technology, Easwari Engineering College, Ramapuram, India; Corresponding author. Department of Computer Science and Engineering, SRM Institute of Science &Technology, Kattankulathur, India.Department of Data Science & Business Systems, SRM Institute of Science & Technology, Kattankulathur, IndiaAuto Capture Drug Text detects (ACDTD) that textual content has currently gained important activities in Pharmaceutical drugs research. The recent studies focused on the different resources of textual content-related messages including social networks. Where a huge quantity of consumers posted information about the usage of pharmaceutical drugs and it's combined and cleared neatly. Our textual content classification strategies depend on regenerating the features of a large set of designs; it represents the semantic rules from the short textual nuggets. Significantly use the detailed structured files and it is combined by instruction data from the various collections to regulate the classification approach. Social Media Online Natural Language Processing (SMONLP) with ACDTD class achieves the F-scores of 0.90, 0.738, and 0.878 for the three information files. Integrating the instruction messages from various compatible files enhances the ACDTD F-scores for the in-house information files to 0.797 and 0.854 respectively. The heterogeneous textual content training using the cases where information sets are maintained may reduce the cost and time in the future.http://www.sciencedirect.com/science/article/pii/S2665917422001842Automatic detectionAuto Capture drug text detectsNLPSocial media monitoring data |
| spellingShingle | P.M. Lavanya E. Sasikala Auto capture on drug text detection in social media through NLP from the heterogeneous data Measurement: Sensors Automatic detection Auto Capture drug text detects NLP Social media monitoring data |
| title | Auto capture on drug text detection in social media through NLP from the heterogeneous data |
| title_full | Auto capture on drug text detection in social media through NLP from the heterogeneous data |
| title_fullStr | Auto capture on drug text detection in social media through NLP from the heterogeneous data |
| title_full_unstemmed | Auto capture on drug text detection in social media through NLP from the heterogeneous data |
| title_short | Auto capture on drug text detection in social media through NLP from the heterogeneous data |
| title_sort | auto capture on drug text detection in social media through nlp from the heterogeneous data |
| topic | Automatic detection Auto Capture drug text detects NLP Social media monitoring data |
| url | http://www.sciencedirect.com/science/article/pii/S2665917422001842 |
| work_keys_str_mv | AT pmlavanya autocaptureondrugtextdetectioninsocialmediathroughnlpfromtheheterogeneousdata AT esasikala autocaptureondrugtextdetectioninsocialmediathroughnlpfromtheheterogeneousdata |