Statistical Approaches to Concept-Level Sentiment Analysis

Polarity Detection of Online Reviews Using Sentiment Concepts: NCU IISR Team at ESWC-14 Challenge on Concept-Level Sentiment Analysis

With the introduction of social networks, blogs, wikis, etc., the users’ behavior and their interaction in the Web have changed. As a consequence, people express their opinions and sentiments in a totally different way with respect to the past. All this information hinders potential business opportunities, especially within the advertising world, and key stakeholders need to catch up with the l...

Sentiment 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...

Sentiment analysis methods in Sentiment analysis methods in Persian text: A survey

With the explosive growth of social media such as Twitter, reviews on e-commerce website, and comments on news websites, individuals and organizations are increasingly using opinions in these media for their decision making. Sentiment analysis is one of the techniques used to analyze userschr('39') opinions in recent years. Persian language has specific features and thereby requires unique meth...

Concept-Level Sentiment Analysis using Dependency-based Semantic Parsing : A novel approach

Concept-level sentiment analysis is always superior to word-level analysis as it preserves the semantics associated with multi-word expressions. It offers a better understanding of text and helps to significantly increase the performance of sentiment analysis model. Concept extraction from unstructured text is a key step in concept-level sentiment analysis. In this paper, we propose a novel con...

Knowledge-Based Approaches to Concept-Level Sentiment Analysis

Supervised and semi-supervised statistical models for word-based sentiment analysis

Ever since its inception, sentiment analysis has relied heavily on methods that use words as their basic unit. Even today, such methods deliver top performance. This way of representing data for sentiment analysis is known as the clue model. It offers practical advantages over more sophisticated approaches: It is easy to implement and statistical models can be trained efficiently even on large ...