000			03086nam a22002657a 4500
003			KOHA
005			20250110130645.0
008			240927d2024 cy e|||| |||| 00| 0 eng d
040			_aCY-NiCIU _beng _cCY-NiCIU _erda
041			_aeng
090			_aYL 3559 _bK35 2024
100	1		_aAl-Kaladi, Bandar Khaled Mohammed
245	1	0	_aIDENTIFYING HARMFUL TWEETS: COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS / _cBANDAR KHALED MOHAMMED AL-KALADI ; SUPERVISOR, ASST. PROF. DR. KIAN JAZAYERI
264			_c2024
300			_a46 sheets ; _c30 cm _e+1 CD ROM
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
502			_aThesis (MSc) - Cyprus International University. Institute of Graduate Studies and Research Information Technologies
520			_aSentiment analysis on platforms along the lines of Twitter provides actual perception into public opinions and trends. This study evaluated the performance of one deep learning model, the Gated Recurrent Unit, and three machine learning algorithms, Support Vector Machine, Random Forest, and Naive Bayes, in differentiating between normal and harmful tweets. The dataset, sourced from Twitter, goes through preprocessing in order to remove noise, including user mentions, hashtags, links, and stop words. The cleaned dataset was split into training and testing sets in order to train each classifier. Performance metrics such as accuracy, precision, recall, F1-score, and confusion matrices were used in order to evaluate each model. An accuracy of 92.02% was accomplished by Naive Bayes, with normal tweets possessing a precision of 0.96 and recall of 0.88, and harmful tweets possessing a precision of 0.89 and recall of 0.96. Random Forest accomplished the highest accuracy at 97.28%, with normal tweets possessing a precision of 0.98 and recall of 0.97, and harmful tweets possessing a precision of 0.97 and recall of 0.98. Support Vector Machine (SVM) accomplished an accuracy of 96.92%, with normal tweets having a precision of 0.96 and recall of 0.98, and harmful tweets possessing a precision of 0.98 and recall of 0.96. The Gated Recurrent Unit (GRU) achieved an accuracy of 95.02%, with normal tweets having a precision of 0.98 and recall of 0.92, and harmful tweets possessing a precision of 0.92 and recall of 0.98. Each algorithm had advantages and disadvantages: Naive Bayes expressed high precision but lower recall for harmful tweets. Random Forest displayed balanced precision and recall. SVM achieved high accuracy with strong performance in both precision and recall. And GRU successfully handled sequential patterns. The findings highlight the relative advantages and limitations of deep learning and machine learning perspectives in Twitter sentiment analysis, with Random Forest and Support Vector Machine appearing as the majority of its effective among the evaluated methods.
650		0	_aInformation Technologies _vDissertations, Academic
700	1		_aJazayeri, Kian _esupervısor
942			_2ddc _cTS
999			_c293078 _d293078