Semi-Supervised Target-Dependent Sentiment Classification for Micro-Blogs

Authors
Abudalfa, Shadi I.; Ahmed, Moataz A.
Publication Year
2019
Language
English
Format
article
Status
Published version
Description
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. Semisupervised 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.
Facultad de Informática
Subject
Ciencias Informáticas
Access level
Open access
License
Creative Commons Attribution 4.0 International (CC BY 4.0)
Repository
SEDICI (UNLP)
Institution
Universidad Nacional de La Plata
OAI Identifier
oai:sedici.unlp.edu.ar:10915/74462