The advent of Web 2.0 and its continuous growth has yielded enormous amounts of freely
available user-generated information. Within this information, it is easy to find subjective …

The opinion seeking behavior of people for good decision making has greatly enhanced the
importance of social media as a platform for exchange of information. This trend has led to a …

The new transformed read-write Web has resulted in a rapid growth of user generated
content on the Web resulting into a huge volume of unstructured data. A substantial part of …

In this work, we present OntoSenticNet, a commonsense ontology for sentiment analysis
based on SenticNet, a semantic network of 100,000 concepts based on conceptual …

Multi-domain sentiment analysis consists in estimating the polarity of a given text by
exploiting domain-specific information. One of the main issues common to the approaches …

Sentiment analysis is a trending topic that has not yet exhausted its attractiveness, despite
the huge research effort carried out in the last 15 years. One of the most promising directions …

Sentiment analysis is a very popular application area of text mining and machine learning.
The popular methods include support vector machine, naive bayes, decision trees, and …

One of the most important opinion mining research directions falls in the extraction of
polarities referring to specific entities (aspects) contained in the analyzed texts. The …

Sentic computing is a multi-disciplinary approach to sentiment analysis at the crossroads
between affective computing and commonsense computing, which exploits both computer …

In this study, a neutrosophic normal cloud (NNC) and several other related concepts,
including a backward cloud generator, two aggregated operators, and an NNC distance …