Discovering Power Laws in Entity Length

This paper presents a discovery that the length of the entities in various datasets follows a family of scale-free power law distributions. The concept of entity here broadly includes the named entity, entity mention, time expression, aspect term, and domain-specific entity that are well investigated in natural language processing and related areas. The entity length denotes the number of words in an entity. The power law distributions in entity length possess the scale-free property and have well-defined means and finite variances. We explain the phenomenon of power laws in entity length by the principle of least effort in communication and the preferential mechanism

Semi-supervised learning for big social data analysis

In an era of social media and connectivity, web users are becoming increasingly enthusiastic about interacting, sharing, and working together through online collaborative media. More recently, this collective intelligence has spread to many different areas, with a growing impact on everyday life, such as in education, health, commerce and tourism, leading to an exponential growth in the size of the social Web. However, the distillation of knowledge from such unstructured Big data is, an extremely challenging task. Consequently, the semantic and multimodal contents of the Web in this present day are, whilst being well suited for human use, still barely accessible to machines. In this work, we explore the potential of a novel semi-supervised learning model based on the combined use of random projection scaling as part of a vector space model, and support vector machines to perform reasoning on a knowledge base. The latter is developed by merging a graph representation of commonsense with a linguistic resource for the lexical representation of affect. Comparative simulation results show a significant improvement in tasks such as emotion recognition and polarity detection, and pave the way for development of future semi-supervised learning approaches to big social data analytics

A review of sentiment analysis research in Arabic language

Sentiment analysis is a task of natural language processing which has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language