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

Successes and challenges of Arabic sentiment analysis research: a literature review

The analysis of sentiment in text has mainly been focused on the English language. The complexity of the Arabic language and its linguistic features that oppose those found in English resulted in the inability to adapt extant research to Arabic contexts limiting advancement in Arabic sentiment analysis. The need for Arabic sentiment analysis research is accentuated by the driving changes in different Arab regions like heavy political movements in some areas and fast growth in others. These changes help shape not just policies and implications of this region but affect the entire world on a global scale. Therefore, it is essential to utilise effective methods of sentiment analysis to analyse Arabic tweets to understand regional and global implications in microblogging mediums such as Twitter. In this paper, we conduct a comprehensive review of Arabic sentiment analysis, present the pros and cons of the different approaches used and highlight the challenges of it. Finally, we outline the relevant gaps in the literature and suggest recommendations for future Arabic sentiment analysis research. - 2017, Springer-Verlag GmbH Austria.This publication was made possible by the NPRP award [NPRP 7-1334-6-039 PR3] from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the author[s].Scopu

Positive sentiments as coping mechanisms and path to resilience: the case of Qatar blockade

Existing research on coping accentuates the role of positive emotions as defensive mechanisms to cope with stressful situations and the ensuing negative emotions. The same literature justifies the long-term effects of positive emotions that help build lasting resilience. Grounded in theories of coping and resilience, this paper (1) identifies the emotions that people actuate to cope with adversaries and (2) evaluates the resulting long-lasting adaptation and resilience. To do this, we examined the emotions felt by Qatar residents due to a land, sea, and air blockade enforced by neighbouring counties. Accordingly, we analysed 160,000 Arabic tweets originating from Qatar between June-2017 and March-2018 using a novel machine-learning algorithm termed Weighted Conditional Probability. Our algorithm achieved state-of-the-art performance when compared with the often-used Support Vector Machine, Naïve Bayes and Deep Neural Nets algorithms. Results show that, while Qatar residents experienced an emotional roller coaster during the blockade, they used positive emotions like love and optimism to cope with adversities and accompanying emotions of fear and anger. Moreover, our analysis reveals that their adaptive resilient capacities gradually strengthened during the nine months of blockade. The study supports the renowned theory of positive emotions using an advanced methodology and a large-scale dataset