Effects of Language Variety and Word Availability in Commercial Advertisements on Listener’s Lexical Recall

Commercial advertisements in Arabic-speaking regions tend to alternate between dialectal Arabic and Modern Standard Arabic, but it is not yet clear whether language variety has any impact on listener’s lexical recall. Insight into this issue should help enterprises design their commercial advertisements in a linguistically intelligent manner. This study addresses two questions: 1) How does language variety (dialectal vs. standard) affect listener’s lexical recall in commercial advertisements? 2) Do listeners recall words that have appeared in dialectal advertisements better than those that did not appear in advertisements using the same variety? Fifteen Saudi participants responded to a forced-choice memory test with 24 yes-no questions (3 per advertisement) asking participants to report whether they heard a specific key word in eight advertisements that utilized different language varieties. The findings show that Arabic speakers tend to perceive both Modern Standard Arabic and dialectal Arabic in commercial advertisements similarly, but tend to recall the presence of a key word in an advertisement better than its absence. Future research may increase the sample size and examine more Arabic varieties.

Effects of Language Variety and Word Availability in Commercial Advertisements on Listener’s Lexical Recall

Commercial advertisements in Arabic-speaking regions tend to alternate between dialectal Arabic and Modern Standard Arabic, but it is not yet clear whether language variety has any impact on listener’s lexical recall. Insight into this issue should help enterprises design their commercial advertisements in a linguistically intelligent manner. This study addresses two questions: 1) How does language variety (dialectal vs. standard) affect listener’s lexical recall in commercial advertisements? 2) Do listeners recall words that have appeared in dialectal advertisements better than those that did not appear in advertisements using the same variety? Fifteen Saudi participants responded to a forced-choice memory test with 24 yes-no questions (3 per advertisement) asking participants to report whether they heard a specific key word in eight advertisements that utilized different language varieties. The findings show that Arabic speakers tend to perceive both Modern Standard Arabic and dialectal Arabic in commercial advertisements similarly, but tend to recall the presence of a key word in an advertisement better than its absence. Future research may increase the sample size and examine more Arabic varieties.

Hungarian place names from a Moroccan perspective

Hungarian, or "Magyar", is a Finno-Ugric language that is different from the other European languages. Despite existing within an Indo-European environment and experiencing some Latinization (Indo-Europeanization), it has retained its distinct characteristics. Nevertheless, it also has some linguistic features, such as a phonetic structure that carries no specific sounds that cannot be easily uttered by a French, Italian, German, or English speaker, rendering it relatively easier for speakers of some Indo-European languages. On the other hand, Morocco has a multilingual environment, with Standard Arabic and Berber (Amazigh) as official languages, along with French and dialectal Arabic. Thus, the coexistence of these languages allowed for a bilingual representation of place names; an Arabic endonym and a French exonym. Both variants hold an official status and are used in maps and road signs. Therefore, the goal of this study is to record Moroccans' pronunciation of Hungarian place names. It is worth investigating whether such Arabic speakers with French knowledge will have difficulty reading the Hungarian toponyms and what is the reasoning behind such difficulty.

«Siftlikom msg f tel opostitha hna rj3 l history»: mescolanze di codice nella comunicazione mediale in Marocco

Based on first-hand collected data, the article analyses a number of code-switching occurrences in multilingual chats among a community of English teachers in the Fes-Meknes region of Morocco. The data are compared with the results of a perceptual questionnaire on linguistic self-assessments and also take into account the orthographic aspect of the messages. The complex sociolinguistic framework of the area vividly emerges, as well as the real and perceived status of the varieties and the relationships between codes. The result is a coherent combination of Standard Arabic, dialectal Arabic, French and English.

Indexicality, semanticity and contact along the now < this time pathway

This study presents data from modern Arabic innovations now < this time to investigate the cross-linguistic developmental pathway temporal deictic < [demonstrative [time noun]]. Products of this path (e.g., German heute, Spanish ahora) feature consistently in contrastive approaches to grammaticalization and lexicalization and have been advanced as exclusive examples of both phenomena, without clear resolution. In this investigation, I establish the derivation of now forms in dialectal Arabic from ten distinct [demonstrative [time noun]] source constructions and identify patterns of fusion and coalescence relevant to both grammaticalization and lexicalization analyses. I then demonstrate a correlated progression of indexicalization and desemanticization in these items’ semanto-pragmatic structure that firmly positions them as examples of grammaticalizing, rather than lexicalizing, change, and proceed to develop this account via examination of the cross-dialectal diffusion of now < this time as an abstract, schematized structure. This approach provides additional support for a grammaticalization account of temporal deictic < [demonstrative [time noun]] developments cross-linguistically and elaborates a novel evidentiary stream with implications for the integration of contact linguistics and grammaticalization/lexicalization studies more broadly.

A Historical Reconstruction of Some Pronominal Suffixes in Modern Dialectal Arabic

The morphology of the pronominal suffixes in dialectal Arabic are of particular interest for scholars of the history of Arabic for two main reasons. First, multiple dialects attest suffixes that, from a comparative perspective, apparently retain final short vowels. The second and more complicated issue concerns the vowels which precede the suffixes in the dialects, which are thought to either have been case inflecting or epenthetic. In this paper, I take up Jean Cantineau’s “embarrassing question” of how to account for the development of the vowels of the pronominal suffixes. Based on data from dialectal tanwīn in modern dialects, and attestations from pre-modern texts as well, I will argue that the pre-suffix vowels did originate in case inflecting vowels, but that no historical model heretofore proposed can satisfactorily account for how the various dialectal forms might have arisen. I identify two major historical developments and propose models for each. First, I suggest that dialects in which the pre-suffixal vowels harmonized with the suffix vowels developed via a process of harmonization across morpheme boundaries before the loss of final short vowels. For dialects in which one vowel is generalized, I argue that a post-stress neutralization took place, which led to a single vowel both before suffixes and tanwīn as well. Finally, I rely on evidence from the behavior of the suffixes to argue that the final vowel of the 3fs suffix was originally long, but that those of the 3ms, 2ms, and 2fs were most likely short.

Creation of annotated country-level dialectal Arabic resources: An unsupervised approach

The wide usage of multiple spoken Arabic dialects on social networking sites stimulates increasing interest in Natural Language Processing (NLP) for dialectal Arabic (DA). Arabic dialects represent true linguistic diversity and differ from modern standard Arabic (MSA). In fact, the complexity and variety of these dialects make it insufficient to build one NLP system that is suitable for all of them. In comparison with MSA, the available datasets for various dialects are generally limited in terms of size, genre and scope. In this article, we present a novel approach that automatically develops an annotated country-level dialectal Arabic corpus and builds lists of words that encompass 15 Arabic dialects. The algorithm uses an iterative procedure consisting of two main components: automatic creation of lists for dialectal words and automatic creation of annotated Arabic dialect identification corpus. To our knowledge, our study is the first of its kind to examine and analyse the poor performance of the MSA part-of-speech tagger on dialectal Arabic contents and to exploit that in order to extract the dialectal words. The pointwise mutual information association measure and the geographical frequency of word occurrence online are used to classify dialectal words. The annotated dialectal Arabic corpus (Twt15DA), built using our algorithm, is collected from Twitter and consists of 311,785 tweets containing 3,858,459 words in total. We randomly selected a sample of 75 tweets per country, 1125 tweets in total, and conducted a manual dialect identification task by native speakers. The results show an average inter-annotator agreement score equal to 64%, which reflects satisfactory agreement considering the overlapping features of the 15 Arabic dialects.

Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.