|
Title:
|
A LEARNING-BASED KEYWORD EXTRACTION TECHNIQUE FOR MULTILINGUAL SOCIAL MEDIA COMMENTS |
|
Author(s):
|
Kishan Kumar Ganguly, Amit Seal Ami and Md. Mahbubul Alam Joarder |
|
ISBN:
|
978-989-8533-77-7 |
|
Editors:
|
Mário Macedo and Piet Kommers |
|
Year:
|
2018 |
|
Edition:
|
Single |
|
Keywords:
|
Facebook, Ranking, Decision Tree, Social Media |
|
Type:
|
Full Paper |
|
First Page:
|
149 |
|
Last Page:
|
156 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Due to the large user base in social media, social media posts and comments hold discussions on trending issues. Different Government and Non-Government organizations actively maintain social media accounts where many people engage and discuss their problems. This provides a great opportunity to systematically arrange and present these problems topic-wise which helps to direct these into appropriate recipients. Previous works provide techniques on topic extraction, sentiment classification, opinion mining from natural language text. However, these techniques are neither applied to social media nor do consider the multilingual aspect of social media. In this paper, a learning-based technique is proposed that extracts important keywords from multilingual social media interactions and provides a ranked list of these under predefined topics. The proposed technique is evaluated on Bangla, English and French comments from well-known and/or verified Facebook pages. It was observed that a high accuracy both in case of keyword extraction and ranking can be achieved by utilizing the proposed approach. |
|
|
|
|
|
|
