To search, Click below search items.

 

All Published Papers Search Service

Title

Classifying Social Media Users’ Stance: Exploring Diverse Feature Sets Using Machine Learning Algorithms

Author

Kashif Ayyub, Muhammad Wasif Nisar, Ehsan Ullah Munir, and Muhammad Ramzan

Citation

Vol. 24  No. 2  pp. 79-88

Abstract

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Na?ve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Na?ve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

Keywords

Sentiment analysis, Stance classification, Emotion, Opinion Mining, Feature set, Machine Learning.

URL

http://paper.ijcsns.org/07_book/202402/20240209.pdf