Charibeth Cheng

Social media and civic engagement during calamities: the case of Twitter use during typhoon Yolanda

Abstract This study explores the nature and promise of citizen engagement in Twitter during calamities, specifically in the context of typhoon Yolanda. Through topic modeling and content analysis, the article explores the “acts of civic engagement” expressed in disaster tweets and how the character of the tweets changed over a five-month period. The article adopts the actualizing citizenship model which offers an alternative understanding of citizenship as performative and which is relevant for the analysis of social media engagement by citizens. The article concludes with a reflection of the nature and typology of Twitter-mediated communication during calamities, highlighting the active engagement of celebrities in disaster tweets, and analysis of whether and how these can be construed as acts of civic engagement.

Philippine Language Resources: Trends and Directions

We present the diverse research activities on Philippine languages from all over the country, with focus on the Center for Language Technologies of the College of Computer Studies, De La Salle University, Manila, where majority of the work are conducted. These projects include the formal representation of Philippine languages and the processes involving these languages. Language representation entails the manual and automatic development of language resources such as lexicons and corpora for various human languages including Philippine languages, across various forms such as text, speech and video files. Tools and applications on languages that we have worked on include morphological processes, part of speech tagging, language grammars, machine translation, sign language processing and speech systems. Future directions are also presented.

Feature-Based Subjectivity Classification of Filipino Text

Subjectivity classification classifies whether a text expresses an opinion or not. Though there are already existing works in this field especially for the English Language, no reports have been made if these approaches are indeed effective when adapted to the Filipino language. This research reports a feature-based approach for subjectivity classification using existing classifiers such as Naïve Bayes, Bagging, Multilayer perceptron and Random Forest Tree. Result shows that the Bagging classifier gave the best results with 64.7% accuracy.

Building language resources for a Multi-Engine English-Filipino machine translation system

In this paper, we present the building of various language resources for a multi-engine bi-directional English-Filipino Machine Translation (MT) system. Since linguistics information on Philippine languages are available, but as of yet, the focus has been on theoretical linguistics and little is done on the computational aspects of these languages, attempts are reported here on the manual construction of these language resources such as the grammar, lexicon, morphological information, and the corpora which were literally built from almost non-existent digital forms. Due to the inherent difficulties of manual construction, we also discuss our experiments on various technologies for automatic extraction of these resources to handle the intricacies of the Filipino language, designed with the intention of using them for the MT system. To implement the different MT engines and to ensure the improvement of translation quality, other language tools (such as the morphological analyzer and generator, and the part of speech tagger) were developed.

NormAPI: An API for normalizing Filipino shortcut texts

As the number of Internet and mobile phone users grow, texting and chatting have become popular means of communication. Reaching new heights, the extensive use of cellphones and Internet led into the creation of a new language, where words are transformed and made shorter using various styles. Shortcut texting is used in informal venues such as SMS, online, chat rooms, forums and posts in social networks. Huge amounts of data originating from these informal sources can be utilized for various tasks in machine learning and data analytics. As these data may be written in shortcut forms, text normalization is necessary before NLP actions such as information extraction, data mining, text summarization, opinion classification, and even bilingual translations can be fully achieved, by acting as a preprocessing stage that transforms all informal texts back to their original and more understandable forms. This paper is about NormAPI, an API for normalizing Filipino shortcut texts. NormAPI primarily intends to be used as a preprocessing system that corrects informalities in shortcut texts before they are handed for complete data processing.