Ant colony optimization algorithm for feature selection in sentiment analysis of social media data (Record no. 22720)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250425101708.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250425b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 20464 |
| Author | Kavitha, P. |
| 245 ## - TITLE STATEMENT | |
| Title | Ant colony optimization algorithm for feature selection in sentiment analysis of social media data |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.14(4), Apr |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Chennai |
| Name of publisher, distributor, etc. | ICT Academy |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 3334-3339p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Sentiment analysis of social media data involves extracting valuable<br/>insights from vast amounts of unstructured text. Feature selection<br/>plays a crucial role in enhancing the accuracy and efficiency of<br/>sentiment analysis algorithms. This study proposes the application of<br/>the Ant Colony Optimization (ACO) algorithm for feature selection in<br/>sentiment analysis. ACO is inspired by the foraging behavior of ants<br/>and has been successfully applied to various optimization problems. In<br/>this context, ACO is utilized to select the most informative features from<br/>the dataset, thereby improving the performance of sentiment analysis<br/>models. The contribution of this research lies in the adaptation of ACO<br/>for feature selection in sentiment analysis of social media data. By<br/>leveraging the inherent strengths of ACO, such as its ability to explore<br/>large solution spaces and adapt to dynamic environments, more<br/>accurate sentiment analysis models can be developed. Experimental<br/>results demonstrate that the proposed ACO-based feature selection<br/>approach outperforms traditional methods in terms of classification<br/>accuracy and computational efficiency. The selected features exhibit<br/>strong predictive power, leading to improved sentiment analysis<br/>performance on social media data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 25995 |
| Co-Author | Lalitha, S. D. |
| 773 0# - HOST ITEM ENTRY | |
| Title | ICTACT Journal on Soft Computing (IJSC) |
| Place, publisher, and date of publication | Chennai ICT Academy |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://ictactjournals.in/paper/IJSC_Vol_14_Iss_4_Paper_5_3334_3339.pdf |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 25/04/2025 | 2025-0669 | 25/04/2025 | 25/04/2025 | Articles Abstract Database |