In recent years, the massive diffusion of social networks has made available a large amount
of user-generated content, for the most part in the form of textual data that contain people's …

In the paper the word-level n-grams based approach is proposed to find similarity between
texts. The approach is a combination of two separate and independent techniques: self …

Traditional lexicon-based approaches for sentiment analysis are usually not able to model
negation, as they do not provide proper techniques to identify the right negation window. In …

Introduction Predicting the sentiments and emotions of people from their texts is a critical
issue in cognitive computing. The explosive growth of social network services has led to a …

With the widespread use of social media, companies now have access to a wealth of
customer feedback data which has valuable applications to Customer Relationship …

This paper presents an unsupervised method for aspect level sentiment analysis that uses
the Growing Hierarchical Self-organizing Maps. Different sentences in a product review refer …

The paper approaches aspect based opinion mining, which uses an unsupervised neural
network as the opinion classifier. The neural network is an extension of the Growing …

This paper introduces a general Bayesian framework for obtaining sparse solutions to
regression predicting, and the practical model'relevance vector machine'(RVM) by Michael …

Due to the increased use of social media in the past few years, a large volume of data has
been accumulated which contains human sentiments and opinions. The field that deals with …

Determining the sentiment of a written text is an important task in text research. This task can
be performed either in the supervised or unsupervised version. In this paper, we propose a …