Joey

Why use "return_tensors="?

Emotion ontologies have been developed to capture affect, a concept that encompasses discrete emotions and feelings, especially for research on sentiment analysis, which analyzes a customer's attitude towards a company or a product. However, there have been limited efforts to adapt and employ these ontologies. This research surveys and synthesizes emotion ontology studies to develop a Framework of Emotion Ontologies that can be used to help a user select or design an appropriate emotion ontology to support sentiment analysis and increase the user's understanding of the roles of affect, context, and behavioral information with respect to sentiment. The framework, which is derived from research on emotion ontologies, psychology, and sentiment analysis, classifies emotion ontologies as discrete emotion or one of two hybrid ontologies that are combinations of the discrete, dimensional, or componential process emotion paradigms. To illustrate its usefulness, the framework is applied to the development of an emotion ontology for a sentiment analysis application.

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.