Abstract
The wealth of opinions expressed in micro-blogs, such as tweets, motivated researchers to develop techniques for automatic opinion detection. However, accuracies of such techniques are still limited. Moreover, current techniques focus on detecting sentiment polarity regardless of the topic (target) discussed. Detecting sentiment towards a specific target, referred to as target-dependent sentiment classification, has not received adequate researchers’ attention. Literature review has shown that all target-dependent approaches use supervised learning techniques. Such techniques need a large number of labeled data. However, labeling data in social media is cumbersome and error prone. The research presented in this paper addresses this issue by employing semi-supervised learning techniques for target-dependent sentiment classification. Semi-supervised learning techniques make use of labeled as well as unlabeled data. In this paper, we present a new semi-supervised learning technique that uses less number of labeled micro-blogs than that used by supervised learning techniques. Experiment results have shown that the proposed technique provides comparable accuracy.
Original language | English |
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Article number | e06 |
Pages (from-to) | 55-65 |
Number of pages | 11 |
Journal | Journal of Computer Science and Technology(Argentina) |
Volume | 19 |
Issue number | 1 |
DOIs | |
State | Published - Apr 2019 |
Bibliographical note
Publisher Copyright:© 2019, Facultad de Informatica, Universidad Nacional de La Plata. All rights reserved.
Keywords
- Polarity Classification
- Semi-Supervised Learning
- Sentiment Analysis
- Social Opinions
- Target-Dependent
ASJC Scopus subject areas
- Software
- Computer Science (miscellaneous)
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Artificial Intelligence