Which Configuration Works Best? An Experimental Study on Supervised Arabic Twitter Sentiment Analysis

With the recently grown attention from different research communities for opinion mining, there is an evolving body of work on Arabic Sentiment Analysis (ASA). This paper introduces a systematic review of the existing literature relevant to ASA. The main goals of the review are to support research, to propose further areas for future studies in ASA, and to smoothen the progress of other researchers’ search for related studies. The findings of the review propose a taxonomy for sentiment classification methods. Furthermore, the limitations of existing approaches are highlighted in the preprocessing step, feature generation, and sentiment classification methods. Some likely trends for future research with ASA are suggested in both practical and theoretical aspects.

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“…ere are 22 studies in which NB accuracy outperforms SVM [52,62,83,106,118]. NB has the ability to classify sentiments using a small training set.…”

Section: Machine Learning Approachmentioning

confidence: 99%

Arabic Sentiment Analysis: A Systematic Literature Review

Ghallab

Mohsen

Ali

2020

Applied Computational Intelligence and Soft Computing

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“…Therefore, overcoming Twitter API limitations was required. The previous limitations can be overcome using tools which use Twitter advanced search interface to get tweets [26]. The query parameters to such a tool involves a list of keywords, hashtags, exact phrases, Twitter accounts, the start and end date, amongst others.…”

Section: B Datasetmentioning

confidence: 99%

Startup Initiative Response Analysis (SIRA) Framework for Analyzing Startup Initiatives on Twitter

et al. 2020

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Social Media (SM) platforms, particularly Twitter, have become useful tools for startup companies (henceforth startups) which use the latter to support most of their business activities. As a result, there is a need to gauge the performance of specific business initiatives vis-à-vis public sentiment, or more specifically the spread of such initiatives based on Twitter user-generated content. Previous research which makes use of Twitter analysis to analyze the business activities of startups is minimal, especially for Twitter user content in the Arabic language. Consequently, this paper proposes an analytics-based framework called Startup Initiatives Response Analysis (SIRA) designed to assess the performance of initiatives launched by startups via text classification, sentiment analysis, and statistical analysis techniques. To provide empirical evidence for the viability of the proposed research framework, this paper examined the case of an Arab transportation network startup, carrying out a SIRA analysis of an initiative undertaken by Careem to empower women by encouraging them to work for the company. The results confirm the effectiveness of the proposed framework for statistically measuring the initiative spread and the public feedback based on the user-generated content on the Twitter social platform.

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“…The HAAD [35] minimized and utilized LABR corpus [36]. The authors [37] used the corpus of [38]. [39] utilize the corpus created in [40].…”

Section: Corpora and Datasetsmentioning

confidence: 99%