Recall results of four algorithms.
<div><p>Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1852025129835429888 |
|---|---|
| author | Nan Xu (107466) |
| author2 | Yifeng Wang (117830) |
| author2_role | author |
| author_facet | Nan Xu (107466) Yifeng Wang (117830) |
| author_role | author |
| dc.creator.none.fl_str_mv | Nan Xu (107466) Yifeng Wang (117830) |
| dc.date.none.fl_str_mv | 2024-11-18T18:30:22Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0310894.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Recall_results_of_four_algorithms_/27848158 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shortest path algorithm research results show recurrent neural network public opinion risk convolutional neural network classification effect measurement average precision rate 30 %, 79 uses attention mechanism hybrid dynamic deletion div >< p sentiment classification model classify network text dynamic deletion attention mechanism recall rate long text classification speed classification model 84 %, 55 %, 53 %, superior performance innovatively constructs decision model |
| dc.title.none.fl_str_mv | Recall results of four algorithms. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment classification model based on the variant of convolutional neural network and recurrent neural network, and secondly, uses attention mechanism to classify the model. improvement of the classification model by using the attention mechanism. The research results show that the average precision rate, recall rate, and F-value of the dynamic deletion-shortest path algorithm are 97.30%, 79.55%, and 87.53%, and the classification speed is 397 <i>KB</i>/<i>s</i>, which is better than the traditional shortest path algorithm. In the classification effect measurement of long text, the accuracy and F-value of the recurrent neural network variant model are above 84%, and the accuracy of the text sentiment classification model with the introduction of the attention mechanism is improved by 3.89% compared to the pre-improvement period. In summary, the dynamic deletion-shortest path algorithm proposed in the study and the sentiment classification model with the introduction of the attention mechanism have superior performance and can provide certain application value for campus social network opinion risk decision-making.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_42e2c09f37bf7551e199ddcf07d638b1 |
| identifier_str_mv | 10.1371/journal.pone.0310894.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27848158 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Recall results of four algorithms.Nan Xu (107466)Yifeng Wang (117830)GeneticsScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedshortest path algorithmresearch results showrecurrent neural networkpublic opinion riskconvolutional neural networkclassification effect measurementaverage precision rate30 %, 79uses attention mechanismhybrid dynamic deletiondiv >< psentiment classification modelclassify network textdynamic deletionattention mechanismrecall ratelong textclassification speedclassification model84 %,55 %,53 %,superior performanceinnovatively constructsdecision model<div><p>Aiming at the problem that traditional network public opinion monitoring and searching are inefficient and can easily cause resource waste, the study firstly, through the dynamic deletion-shortest path algorithm to classify network text, and on this basis, innovatively constructs a text sentiment classification model based on the variant of convolutional neural network and recurrent neural network, and secondly, uses attention mechanism to classify the model. improvement of the classification model by using the attention mechanism. The research results show that the average precision rate, recall rate, and F-value of the dynamic deletion-shortest path algorithm are 97.30%, 79.55%, and 87.53%, and the classification speed is 397 <i>KB</i>/<i>s</i>, which is better than the traditional shortest path algorithm. In the classification effect measurement of long text, the accuracy and F-value of the recurrent neural network variant model are above 84%, and the accuracy of the text sentiment classification model with the introduction of the attention mechanism is improved by 3.89% compared to the pre-improvement period. In summary, the dynamic deletion-shortest path algorithm proposed in the study and the sentiment classification model with the introduction of the attention mechanism have superior performance and can provide certain application value for campus social network opinion risk decision-making.</p></div>2024-11-18T18:30:22ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0310894.t001https://figshare.com/articles/dataset/Recall_results_of_four_algorithms_/27848158CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/278481582024-11-18T18:30:22Z |
| spellingShingle | Recall results of four algorithms. Nan Xu (107466) Genetics Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shortest path algorithm research results show recurrent neural network public opinion risk convolutional neural network classification effect measurement average precision rate 30 %, 79 uses attention mechanism hybrid dynamic deletion div >< p sentiment classification model classify network text dynamic deletion attention mechanism recall rate long text classification speed classification model 84 %, 55 %, 53 %, superior performance innovatively constructs decision model |
| status_str | publishedVersion |
| title | Recall results of four algorithms. |
| title_full | Recall results of four algorithms. |
| title_fullStr | Recall results of four algorithms. |
| title_full_unstemmed | Recall results of four algorithms. |
| title_short | Recall results of four algorithms. |
| title_sort | Recall results of four algorithms. |
| topic | Genetics Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified shortest path algorithm research results show recurrent neural network public opinion risk convolutional neural network classification effect measurement average precision rate 30 %, 79 uses attention mechanism hybrid dynamic deletion div >< p sentiment classification model classify network text dynamic deletion attention mechanism recall rate long text classification speed classification model 84 %, 55 %, 53 %, superior performance innovatively constructs decision model |