A graphical framework to detect and categorize diverse opinions from online news
This paper proposes a graphical framework to extract opinionated sentences which highlight different contexts within a given news article by introducing the concept of diversity in a graphical model for opinion detection.We conduct extensive evaluations and find that the proposed modification leads to impressive improvement in performance and makes the final results of the model much more usable. The proposed method (OP-D) not only performs much better than the other techniques used for opinion detection as well as introducing diversity, but is also able to select opinions from different categories (Asher et al. 2009). By developing a classification model which categorizes the identified sentences into various opinion categories, we find that OP-D is able to push opinions from different categories uniformly among the top opinions.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityGeneral ClassificationOpinion MiningSimilar Papers 제목 키워드 기반
Author-aware Aspect Topic Sentiment Model to Retrieve Supporting Opinions from Reviews
User generated content about products and services in the form of reviews are often diverse and even contradictory. This makes it difficult for users to know if an opinion in a review is prevalent or biased. We study the…
Semantic SimilaritySemantic Textual SimilaritySelf-Agreement: A Framework for Fine-tuning Language Models to Find Agreement among Diverse Opinions
Finding an agreement among diverse opinions is a challenging topic in multiagent systems. Recently, large language models (LLMs) have shown great potential in addressing this challenge due to their remarkable capabilitie…
Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models
Fake news detection plays a crucial role in protecting social media users and maintaining a healthy news ecosystem. Among existing works, comment-based fake news detection methods are empirically shown as promising becau…
Fake News DetectionTowards Measuring the Representation of Subjective Global Opinions in Language Models
Large language models (LLMs) may not equitably represent diverse global perspectives on societal issues. In this paper, we develop a quantitative framework to evaluate whose opinions model-generated responses are more si…
Twitter Sentiment Analysis of Covid Vacciness
In this paper, we look at a database of tweets sorted by various keywords that could indicate the users sentiment towards covid vaccines. With social media becoming such a prevalent source of opinion, sorting and ranking…
Sentiment AnalysisTwitter Sentiment Analysis