Combining Sentiment Lexicons of Arabic Terms

Lexicons are dictionaries of sentiment words and their matching polarity. Some comprise words that are numerically scored based on the degree of positivity/negativity of the underlying sentiments. The ranges of scores differ since each lexicon has its own scoring process. Others use labelled words instead of scores with polarity tags (i.e., positive/negative/neutral). Lexicons are important in text mining and sentiment analysis which compels researchers to develop and publish them. Larger lexicons better train sentiment models thereby classifying sentiments in text more accurately. Hence, it is useful to combine the various available lexicons. Nevertheless, there exist many duplicates, overlaps and contradictions between these lexicons. In this paper, we define a method to combine different lexicons. We used the method to normalize and unify lexicon items and merge duplicated lexicon items from twelve lexicons for (in)formal Arabic. This resulted in a coherent Arabic sentiment lexicon with the largest number of terms

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Introduction to the special issue on cross-language algorithms and applications

With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version