GECKA3D: A 3D Game Engine for Commonsense Knowledge Acquisition

Commonsense knowledge representation and reasoning is key for tasks such as artificial intelligence and natural language understanding. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. In this paper, we introduce a novel 3D game engine for commonsense knowledge acquisition (GECKA3D) which aims to collect commonsense from game designers through the development of serious games. GECKA3D integrates the potential of serious games and games with a purpose. This provides a platform for the acquisition of reusable and multi-purpose knowledge and also enables the development of games that can provide entertainment value and teach players something meaningful about the actual world they live in

Music Genre Classification: A Semi-supervised Approach

Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1% accuracy of 10-way classification on real-world audio collections

Sentiment Analysis over Online Product Reviews: A Survey

Prior to the invention of the internet while purchasing any product people used to ask the opinions to his family, friends for particular product. but now a days as the swift increase of usage of the internet, more users are motivated to write their feelings about particulars in the form of comments on different sites like Facebook, twitter, online shopping sites, blogs, etc. this comments are nothing but the sentiments of the users this may be positive, negative or neutral. There are various techniques used for summarizing the customer comments like Data mining, Text clssification, Retrieval of informtaion, and summarizing the text. People tend to write their reviews over a product over different sites. Most of the reviews are critical to conclude so it generates difficulty for usefulness of information. If anyone want to know the impact of the particular post/product then it becomes difficult to read all the comments and to classify it. Sentiment analysis is the ongoing research field in the data mining, Sentiment analysis is also referred as opinion mining. This field mainly deals with classifying the sentiments among different types of comments that are written by various users. This paper is about to discuss different techniques, challenges and applications related to sentiment analysis

Sentiment analysis in Turkish

In this chapter, we give an overview of sentiment analysis problem and present a system to estimate the sentiment of movie reviews in Turkish. Our approach combines supervised learning and lexicon-based approaches, making use of a recently constructed Turkish polarity lexicon called SentiTurkNet. For performance evaluation, we investigate the contribution of different feature sets, as well as the effect of lexicon size on the overall classification performance

Enriching SenticNet polarity scores through semi-supervised fuzzy clustering

10.1109/ICDMW.2012.142Proceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012709-71

Sentence-based sentiment analysis with domain adaptation capability

Sentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domainindependent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We introduce a simple method to adapt a general purpose polarity lexicon to a specific domain. Subsequently, we propose new features to be used in a term polarity based approach to sentiment analysis. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is used to find sentences that may convey better information about the overall review polarity. Therefore, our work is also focused on the sentence-based sentiment analysis differently from the other works. Moreover, we worked on two distinct domains, hotel and Twitter with three different systems which are compared with the existing state-of-the-art approaches in the literature