Table 1

Emotion	Training	Development	Testing
Anger	899	215	609
Anticipation	206	57	158
Disgust	433	106	316
fear	391	94	295
Happiness	605	179	393
Love	562	175	367
Optimism	561	165	344
pessimism	499	125	377
Sadness	842	217	579
Supervise	47	13	38
Trust	120	36	77

Table 1: Description of SemEval-2018 Dataset

Table 2

	MCE without SMOTE			MCE with SMOTE
Feature Size	Precision	Recall	F-measure	Precision	Recall	F-measure
250	78.46	77.18	77.81	80.84	79.84	80.34
500	82.28	81.69	81.98	84.22	83.04	83.63
750	78.42	76.25	77.32	79.97	77.51	78.72
1000	82.19	83.22	82.7	83.58	84.7	84.14
1250	82.93	82.99	82.96	84.7	84.89	84.79
1500	77.57	78.23	77.9	78.88	80.08	79.48
1750	84.6	84.94	84.77	85.96	86.46	86.21
2000	85.17	86.71	85.93	86.82	88.22	87.51
Avg	81.45	81.4	81.42	83.12	83.09	83.1

Table 2: Performance of meta-classifier ensemble (MCE) with and without SMOTE on SemEval-2018 datasets

Tables at a glance

Table 1

Table 2

Figures at a glance

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

FIGURE 1

Figure 1: The proposed methodology of Arabic text-based emotion detection and analysis with imbalanced class problem handling

FIGURE 2

Figure 2: The Pre-Processing Techniques

FIGURE 3

Figure 3: The General Architecture of the Proposed Ensemble

FIGURE 4

Figure 4: Performance of Baseline Classification Models with Proposed Enhanced Methods for Text-Based Arabic Emotion Analysis on SemEval-2018 Corpus

FIGURE 5

Figure 5: Performance of Meta-Classifier Ensemble (MCE) with and Without the SMOTE on SemEval-2018 Dataset