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This research explores the ongoing debate on whether social media sentiment analysis can replace traditional polling and advocates for the combined use of both methods to capture nuanced public perceptions expanding the geographic and linguistic scope of public opinion literature, this study focuses on Kuwait and the Arabic language. It examines public reactions to the Ministry of Higher Education's decision on June 9, 2023, to suspend scholarships for medical students at universities in Jordan and Egypt. The goal was to assess how data from both social media and traditional surveys can aid the government in understanding public opinion towards various policies. A significant contribution of this study is the development of the first multi-label emotion-classification tool specifically designed for Kuwaiti Arabic. This tool's unique capability to interpret and analyze the intricacies of the Kuwaiti dialect provided invaluable insights into local contexts and sentiments, setting a new benchmark for cultural and linguistic specificity in emotion-classification tools. The findings indicate that, while social media alone may not fully convey actual public opinion on political issues, adding survey data yields a richer, multi-dimensional perspective. This approach emphasizes the importance of integrating both methodologies to inform policymaking. The study further highlights the critical role of public opinion in shaping government policies and suggests that using both methods could lead to more informed decisions, enhance public trust, and reduce the likelihood of electoral sanctions.

Objectives: Arabic language is primarily represented in two varieties: Modern Standard Arabic (MSA) and Dialectal Arabic (DA). With the advent of social media, there has been a shift from the predominant use of MSA in writing to the incorporation of DA, thereby generating extensive resources for dialectal text studies. Kuwaiti Arabic (KA), a sub-variety of the Gulf dialect and one of the five principal Arabic dialects, differs significantly from MSA in all linguistic aspects. KA is an under- resourced language with a notable deficiency in language resources. The development of emotion classification tools relies heavily on the availability of resources such as annotated corpora. This study introduces TweetSentKW, a multi-label emotion annotated corpus for KA. Method: TweetSentKW was developed by collecting tweets and selecting relevant emotional classes for the annotation process. Each tweet was annotated by three independent annotators. Results: The TweetSentKW corpus comprises 40,000 manually labeled tweets across various topics. Besides constructing the corpus, this study provides a comprehensive analysis of annotator behavior and the co-occurrences of emotions. The corpus is anticipated to significantly contribute to sentiment analysis research, a crucial method for gauging public opinion. Conclusion: The widespread use of social media platforms, such as Twitter, has led to continuous and uninhibited public expression of opinions on diverse issues. The public and archived nature of these opinions presents a rich opportunity for researchers to analyze and understand public sentiment and perspectives.

Kuwaiti Arabic (KA), like other Arabic dialects, is a spoken variety of Arabic that does not have a standardized written convention contrary to Modern Standard Arabic (MSA). With the emergence and spread of social media platforms, Arabic dialects have found their way into the written medium, and hence a need arose to process them alongside MSA. The biggest challenge facing NLP tools is that dialects do not have consistent written conventions contrary to MSA, and writers expressing their dialects usually follow a phonet- ic writing system, or they write words as they pronounce them. This has opened the door for variations within the same dialect and between dialects and MSA. Furthermore, a pre- requisite for analysing any language or dialect is the presence of clear written conventions. Therefore, efforts have been made to establish written conventions for Arabic dialects, but the Kuwaiti dialect has not received the required attention. The current study offers a prac- tical solution for processing written KA. It identified and extracted the written conventions of KA from natural data collected from over 100K Kuwaiti tweets since they represent a good model of natural language. The morphological analyzer (MADAMIRA)- which is de- signed to process MSA- was enhanced with the extracted criteria. Furthermore, the study involved enriching the analyzer with a dictionary of Kuwaiti terms and vocabulary 'lemmas' collected from the Encyclopaedia of Kuwaiti Arabic and from the most used Kuwaiti words on Twitter (currently X). Providing the analyzer with this dictionary of KA words helps it process KA more accurately. The expanded version of the analyzer (MADAMIRA-KA) is the first of its kind designed entirely to process the Kuwaiti dialect and has achieved excellent performance in analyzing over 100K Kuwaiti tweets successfully. The importance of this study lies in developing such a morphological analyzer, which can be used for automated translation, dialect recognition and sentiment analysis.

Developing natural language processing applications for Arabic must consider the different linguistic characteristics found in speech and translate those characteristics to script in order to reduce computational complexity and therefore reduce the word error rate (WER). Suprasegmental features are fundamental properties of speech that can enhance the performance of many natural speech processing applications. The present study considered stress as a prosodic feature comprising the prominence of syllables in speech by developing a tool that generated phonetic transcriptions and predicted the stress position. The generated transcription was used to create the phonetic dictionary necessary for developing an automatic speech recognition (ASR) system. This tool had to be accurate, linguistically motivated, and applicationally useful; therefore, the effectiveness of the generated stress-marked phonetic dictionary was tested by comparing the performance of a standard fixed dictionary-based system with that of one using the automatically generated dictionary. The research reported a 5.6% reduction in WER when using a dictionary with stress markers attached to each phone in the stressed syllable and a 3.5% reduction in WER when using a dictionary with stress markers assigned only to stressed vowels. These results encourage future studies to employ prosodic features of speech when developing different speech processing applications.