TY - JOUR
T1 - Towards Context-Aware Opinion Summarization for Monitoring Social Impact of News
AU - Ramón Hernández, Alejandro
AU - García Lorenzo, María Matilde
AU - Simón-Cuevas, Alfredo
AU - Arco, Leticia
AU - Serrano-Guerrero, Jesús
PY - 2020/11/18
Y1 - 2020/11/18
N2 - Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions(e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.
AB - Opinion mining and summarization of the increasing user-generated content on different digital platforms (e.g., news platforms) are playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizen’s sentiments for decision-making. Opinion mining provides the sentiment from contents, whereas summarization aims to condense the most relevant information. However, most of the reported opinion summarization methods are conceived to obtain generic summaries, and the context that originates the opinions(e.g., the news) has not usually been considered. In this paper, we present a context-aware opinion summarization model for monitoring the generated opinions from news. In this approach, the topic modeling and the news content are combined to determine the “importance” of opinionated sentences. The effectiveness of different developed settings of our model was evaluated through several experiments carried out over Spanish news and opinions collected from a real news platform. The obtained results show that our model can generate opinion summaries focused on essential aspects of the news, as well as cover the main topics in the opinionated texts well. The integration of term clustering, word embeddings, and the similarity-based sentence-to-news scoring turned out the more promising and effective setting of our model.
KW - opinion mining
KW - opinion summarization
KW - topic modeling
KW - semantic similarity measures
UR - http://www.scopus.com/inward/record.url?scp=85096704299&partnerID=8YFLogxK
U2 - 10.3390/info11110535
DO - 10.3390/info11110535
M3 - Article
VL - 11
SP - 1
EP - 21
JO - Information
JF - Information
SN - 2078-2489
IS - 11
M1 - 535
ER -