AffectiveSpace 2: Enabling Affective Intuition for Concept-Level Sentiment Analysis
نویسندگان
چکیده
Predicting the a↵ective valence of unknown multiword expressions is key for concept-level sentiment analysis. A↵ectiveSpace 2 is a vector space model, built by means of random projection, that allows for reasoning by analogy on natural language concepts. By reducing the dimensionality of a↵ective common-sense knowledge, the model allows semantic features associated with concepts to be generalized and, hence, allows concepts to be intuitively clustered according to their semantic and a↵ective relatedness. Such an a↵ective intuition (so called because it does not rely on explicit features, but rather on implicit analogies) enables the inference of emotions and polarity conveyed by multi-word expressions, thus achieving e cient concept-level sentiment analysis.
منابع مشابه
Sentic Medoids: Organizing Affective Common Sense Knowledge in a Multi-Dimensional Vector Space
Existing approaches to opinion mining and sentiment analysis mainly rely on parts of text in which opinions and sentiments are explicitly expressed such as polarity terms and affect words. However, opinions and sentiments are often conveyed implicitly through context and domain dependent concepts, which make purely syntactical approaches ineffective. To overcome this problem, we have recently p...
متن کاملAn ELM-based model for affective analogical reasoning
Between the dawn of the Internet through year 2003, there were just a few dozens exabytes of information on the Web. Today, that much information is created weekly. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised increasing interest both in the scientific community...
متن کاملSenticNet 2
Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, we developed SenticNet 2, a publ...
متن کاملSentiment Analysis of Social Networking Data Using Categorized Dictionary
Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed. A categorized dictiona...
متن کاملSenticNet 3: A Common and Common-Sense Knowledge Base for Cognition-Driven Sentiment Analysis
SenticNet is a publicly available semantic and affective resource for concept-level sentiment analysis. Rather than using graph-mining and dimensionality-reduction techniques, SenticNet 3 makes use of ‘energy flows’ to connect various parts of extended common and common-sense knowledge representations to one another. SenticNet 3 models nuanced semantics and sentics (that is, the conceptual and ...
متن کامل