Classification of colloquial Arabic tweets in real-time to detect high-risk floods

Twitter has eased real-time information flow for decision makers, it is also one of the key enablers for Open-source Intelligence (OSINT). Tweets mining has recently been used in the context of incident response to estimate the location and damage caused by hurricanes and earthquakes. We aim to research the detection of a specific type of high-risk natural disasters frequently occurring and causing casualties in the Arabian Peninsula, namely `floods'. Researching how we could achieve accurate classification suitable for short informal (colloquial) Arabic text (usually used on Twitter), which is highly inconsistent and received very little attention in this field. First, we provide a thorough technical demonstration consisting of the following stages: data collection (Twitter REST API), labelling, text pre-processing, data division and representation, and training models. This has been deployed using `R' in our experiment. We then evaluate classifiers' performance via four experiments conducted to measure the impact of different stemming techniques on the following classifiers SVM, J48, C5.0, NNET, NB and k-NN. The dataset used consisted of 1434 tweets in total. Our findings show that Support Vector Machine (SVM) was prominent in terms of accuracy (F1=0.933). Furthermore, applying McNemar's test shows that using SVM without stemming on Colloquial Arabic is significantly better than using stemming techniques

Identifying tweets from Syria refugees using a Random Forest classifier

A social unrest and violent atmosphere can force a vast number of people to flee their country. While governments and international aid organizations need migration data to inform their decisions, the availability of this data is often delayed due to the tediousness to collect and publish this data. Recent studies recognized the increasing usage of social networking platforms amongst refugees to seek help and express their hardship during their journeys. This paper investigates the feasibility of accurately extracting and identifying tweets from Syria refugees. A robust framework has been developed to find, retrieve, clean and classify tweets from Syria. This includes the development of a Random Forest classifier, which automatically determines which tweets are from Syria refugees. Testing the classifier with samples of historical Twitter data produced promising result of 81% correct classification rate. This preliminary study demonstrates the potential that refugees’ messages can be accurately identified and extracted from social media data mixed with many unwanted messages, and this enables further works for studying refugee issues and predicting their migration patterns

Sentiment Analysis for the Low-Resourced Latinised Arabic "Arabizi"

The expansion of digital communication mediums from private mobile messaging into the public through social media presented an opportunity for the data science research and industry to mine the generated big data for artificial information extraction. A popular information extraction task is sentiment analysis, which aims at extracting polarity opinions, positive, negative, or neutral, from the written natural language. This science helped organisations better understand the public’s opinion towards events, news, public figures, and products. However, sentiment analysis has advanced for the English language ahead of Arabic. While sentiment analysis for Arabic is developing in the literature of Natural Language Processing (NLP), a popular variety of Arabic, Arabizi, has been overlooked for sentiment analysis advancements. Arabizi is an informal transcription of the spoken dialectal Arabic in Latin script used for social texting. It is known to be common among the Arab youth, yet it is overlooked in efforts on Arabic sentiment analysis for its linguistic complexities. As to Arabic, Arabizi is rich in inflectional morphology, but also codeswitched with English or French, and distinctively transcribed without adhering to a standard orthography. The rich morphology, inconsistent orthography, and codeswitching challenges are compounded together to have a multiplied effect on the lexical sparsity of the language, where each Arabizi word becomes eligible to be spelled in many ways, that, in addition to the mixing of other languages within the same textual context. The resulting high degree of lexical sparsity defies the very basics of sentiment analysis, classification of positive and negative words. Arabizi is even faced with a severe shortage of data resources that are required to set out any sentiment analysis approach. In this thesis, we tackle this gap by conducting research on sentiment analysis for Arabizi. We addressed the sparsity challenge by harvesting Arabizi data from multi-lingual social media text using deep learning to build Arabizi resources for sentiment analysis. We developed six new morphologically and orthographically rich Arabizi sentiment lexicons and set the baseline for Arabizi sentiment analysis on social media

The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist

Compilation of an Arabic Children’s Corpus

Inspired by the Oxford Children's Corpus, we have developed a prototype corpus of Arabic texts written and/or selected for children. Our Arabic Children's Corpus of 2950 documents and nearly 2 million words has been collected manually from the web during a 3-month project. It is of high quality, and contains a range of different children's genres based on sources located, including classic tales from The Arabian Nights, and popular fictional characters such as Goha. We anticipate that the current and subsequent versions of our corpus will lead to interesting studies in text classification, language use, and ideology in children's texts

Annotating for Hate Speech: The MaNeCo Corpus and Some Input from Critical Discourse Analysis

This paper presents a novel scheme for the annotation of hate speech in corpora of Web 2.0 commentary. The proposed scheme is motivated by the critical analysis of posts made in reaction to news reports on the Mediterranean migration crisis and LGBTIQ+ matters in Malta, which was conducted under the auspices of the EU-funded C.O.N.T.A.C.T. project. Based on the realization that hate speech is not a clear-cut category to begin with, appears to belong to a continuum of discriminatory discourse and is often realized through the use of indirect linguistic means, it is argued that annotation schemes for its detection should refrain from directly including the label 'hate speech,' as different annotators might have different thresholds as to what constitutes hate speech and what not. In view of this, we suggest a multi-layer annotation scheme, which is pilot-tested against a binary +/- hate speech classification and appears to yield higher inter-annotator agreement. Motivating the postulation of our scheme, we then present the MaNeCo corpus on which it will eventually be used; a substantial corpus of on-line newspaper comments spanning 10 years.Comment: 10 pages, 1 table. Appears in Proceedings of the 12th edition of the Language Resources and Evaluation Conference (LREC'20