Towards shared datasets for normalization research

In this paper we present a Dutch and English dataset that can serve as a gold standard for evaluating text normalization approaches. With the combination of text messages, message board posts and tweets, these datasets represent a variety of user generated content. All data was manually normalized to their standard form using newly-developed guidelines. We perform automatic lexical normalization experiments on these datasets using statistical machine translation techniques. We focus on both the word and character level and find that we can improve the BLEU score with ca. 20% for both languages. In order for this user generated content data to be released publicly to the research community some issues first need to be resolved. These are discussed in closer detail by focussing on the current legislation and by investigating previous similar data collection projects. With this discussion we hope to shed some light on various difficulties researchers are facing when trying to share social media data

A cloud-based tool for sentiment analysis in reviews about restaurants on TripAdvisor

The tourism industry has been promoting its products and services based on the reviews that people often write on travel websites like TripAdvisor.com, Booking.com and other platforms like these. These reviews have a profound effect on the decision making process when evaluating which places to visit, such as which restaurants to book, etc. In this contribution is presented a cloud based software tool for the massive analysis of this social media data (TripAdvisor.com). The main characteristics of the tool developed are: i) the ability to aggregate data obtained from social media; ii) the possibility of carrying out combined analyses of both people and comments; iii) the ability to detect the sense (positive, negative or neutral) in which the comments rotate, quantifying the degree to which they are positive or negative, as well as predicting behaviour patterns from this information; and iv) the ease of doing everything in the same application (data downloading, pre-processing, analysis and visualisation). As a test and validation case, more than 33.500 revisions written in English on restaurants in the Province of Granada (Spain) were analyse

Social media analytics: a survey of techniques, tools and platforms

This paper is written for (social science) researchers seeking to analyze the wealth of social media now available. It presents a comprehensive review of software tools for social networking media, wikis, really simple syndication feeds, blogs, newsgroups, chat and news feeds. For completeness, it also includes introductions to social media scraping, storage, data cleaning and sentiment analysis. Although principally a review, the paper also provides a methodology and a critique of social media tools. Analyzing social media, in particular Twitter feeds for sentiment analysis, has become a major research and business activity due to the availability of web-based application programming interfaces (APIs) provided by Twitter, Facebook and News services. This has led to an ‘explosion’ of data services, software tools for scraping and analysis and social media analytics platforms. It is also a research area undergoing rapid change and evolution due to commercial pressures and the potential for using social media data for computational (social science) research. Using a simple taxonomy, this paper provides a review of leading software tools and how to use them to scrape, cleanse and analyze the spectrum of social media. In addition, it discussed the requirement of an experimental computational environment for social media research and presents as an illustration the system architecture of a social media (analytics) platform built by University College London. The principal contribution of this paper is to provide an overview (including code fragments) for scientists seeking to utilize social media scraping and analytics either in their research or business. The data retrieval techniques that are presented in this paper are valid at the time of writing this paper (June 2014), but they are subject to change since social media data scraping APIs are rapidly changing

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

Normalization of Dutch user-generated content

Abstract This paper describes a phrase-based machine translation approach to normalize Dutch user-generated content (UGC). We compiled a corpus of three different social media genres (text messages, message board posts and tweets) to have a sample of this recent domain. We describe the various characteristics of this noisy text material and explain how it has been manually normalized using newly developed guidelines. For the automatic normalization task we focus on text messages, and find that a cascaded SMT system where a token-based module is followed by a translation at the character level gives the best word error rate reduction. After these initial experiments, we investigate the system's robustness on the complete domain of UGC by testing it on the other two social media genres, and find that the cascaded approach performs best on these genres as well. To our knowledge, we deliver the first proof-of-concept system for Dutch UGC normalization, which can serve as a baseline for future work