Identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral (Source: Oxford Languages)
Image Source: Deep learning for sentiment analysis: A survey
Discovering the semantic aspects in Incel forums are the main goal of this research; we apply Natural language processing techniques based on topic modeling to latent topic discovery and opinion mining of users from a popular online Incel discussion forum.
Continued population growth and urbanization is shifting research to consider the quality of urban green space over the quantity of these parks, woods, and wetlands.
To understand the important dimensions of service quality from the passenger's perspective and tailor service offerings for competitive advantage, airlines can capitalize on the abundantly available online customer reviews (OCR).
This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio.
Sentiment analysis and opinion mining is an important task with obvious application areas in social media, e. g. when indicating hate speech and fake news.
RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining.
Support or opposition concerning a debated claim such as abortion should be legal can have different underlying reasons, which we call perspectives.
Classifying and resolving coreferences of objects (e. g., product names) and attributes (e. g., product aspects) in opinionated reviews is crucial for improving the opinion mining performance.
An expanding field of substantive interest for the theory of the law and the practice-of-law entails Legal Sentiment Analysis and Opinion Mining (LSAOM), consisting of two often intertwined phenomena and actions underlying legal discussions and narratives: (1) Sentiment Analysis (SA) for the detection of expressed or implied sentiment about a legal matter within the context of a legal milieu, and (2) Opinion Mining (OM) for the identification and illumination of explicit or implicit opinion accompaniments immersed within legal discourse.