Abstract
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.
Highlights
The globalization of the use of the Internet and the development of technologies such as CloudComputing, Internet of Things, social networks, Mobile Computing, and others has favored the increase of user-generated content on the web
Results ofSilhouette the Silhouette measure forthe the two two clustering approaches in the topic detection on the TelecomServ dataset by applying (a) WordNet and (b) word embeddings based semantic on the TelecomServ dataset by applying(a)
We have presented a news-focused opinion summarization approach that was designed according to the conception of extractive and topic-based text summarization methods
Summary
The globalization of the use of the Internet and the development of technologies such as CloudComputing, Internet of Things, social networks, Mobile Computing, and others has favored the increase of user-generated content on the web. The processing of user-generated content on digital platforms (e.g., news platforms) is playing significant roles in the success of government programs and initiatives in digital governance, from extracting and analyzing citizens’ sentiments for decision-making [2]. Information 2020, 11, 535 in increasing research interest in tasks within Natural Language Processing (NLP) such as sentiment analysis, called opinion mining [4]. Opinion summarization is the task of automatically generating summaries for a set of opinions that are related to the same topic or specific target [6]. Summaries generated by several of the reported approaches are focused on specific topics [1,8,9], they are generally identified by looking only at the content in opinionated texts, whereas the context that originates the opinions (e.g., news) is not usually taken into account, being this a weakness. Our work is addressed to the application of these models and resources to cope with opinion summarization challenges
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