Abstract
This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset.
Highlights
Social networks, nowadays, are just like beating hearts—people cannot live without them
Grthame lmanagtiucaagle. fIenaotuurresst:udmy,awney anraelsyezaerdch1e1rsgraumtimlizaetidcal gruralemsmexaetriccaisledfienattuhreeAs ratboic untdweeresttsa:nKdatnheanladnsgiustaegres., IEnnonuarasntdudsiys,tewres,aqnuaelsytzioedn t1o1oglsr,aemxmceapttiicoanl rtuoolelss,efixveercviseerdbs, infithveeAnroaubnics,twpleuertsa:lKwaonradnsd, ismispteerrsa,tEivnenaclaanudses,isNteirdsa, qaucelsatuiosen,toEoljlasr, elextcteeprsti,oanntdooElsa,tf fivleettveerrsb. s, five nouns, plural words, imperative clause, Nidaa clause, Eljar letters, 4. anEdmEoattifolnetatlefres.atures: we focused on Sentiment Analysis (SA) to mine the emotional statuses of the tweet
We believe that utilizing the semantic meaning between tweets will drastically increase the general accuracy of the model
Summary
Nowadays, are just like beating hearts—people cannot live without them. Social networks affect various fields, such as health, marketing, politics, businesses, management, etc. With 330 million users [2], was a fertile source of research in this study. Twitter allows users to share their opinions in short-term messages, with a maximum of 280 characters [3]. Sentiment Analysis (SA) is a vital technique used to gain insight human opinions, emotions, and attitudes regarding particular topics in specific, written languages [4,5,6]. SA is the most actively researched field in Natural Language Processing (NLP) [7], and it is involved in data mining and text mining studies [8]. The influence of social media has increased throughout the years, directly impacting the importance of this field [11]. SA helps provide insight into whether society is positively or negatively impacted by an international or national event [12]
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