## 4 LeSSA--Implementation and Technical Details
### Linguistic Rules
#### 4.2.6 Active Self-Training Based Sentiment Learner
The classification performance achieved by HRPL-SL is shown in Figure 9.

**Experiment 3. **_Evaluation of sentiment learner based on self-training._

In this experiment, we used specific confidence threshold values while predicting the labels of unlabeled data. The confidence values evaluated as thresholds are 0.5, 0.6, 0.7 and 0.8. Those classified examples whose confident score is high are selected and added in the label training set. The selection of classified examples based on low confidence threshold values, i.e., 0.5 and 0.6 decline the learning

Figure 8: This figure shows the classification performance achieved by WCSL-SL on multi-domain datasets. Figure 6.0 This figure shows the classification performance achieved by HRPL-SL on multi-domain datasets. process due to the misclassified examples. Therefore, we adopted the high confident threshold value 0.8 to select more valuable p positive and n negative examples for the complete learning process. Figure 10 shows the performance of the final trained sentiment learner on multi-domain datasets based on self-training with four different confidence threshold values, 0.5, 0.6, 0.7 and 0.8. From Figure 10, it is clear that the final trained sentiment learner based on ST-SL with confident threshold 0.8 achieved good classification performance. **Experiment 4**. : _Evaluation of sentiment learner based on active self-training._

In this experiment, some of the highly-uncertain, most-informative examples (misclassified unlabeled examples) whose confidence score is close to 0.5 are actively selected for manual annotation. According to the method proposed in [70], the misclassified unlabeled examples in active learning may carry more information than correctly classified unlabeled examples. Simultaneously, some of the high-confidence level classified examples with threshold value 0.8 are selected by self-training. The good performance of this approach on multi-domain datasets is due to the selection of classified examples with active learning together with self-training. The classification performance obtained by final sentiment learner based on AST is shown in Figure 6.1

**Experiment 5**. : _Evaluation of sentiment learner based on active self-co-training._

In this experiment, most informative and high confident level classified examples are selected by active learning and self-training, respectively. In details, two different classifiers (S1, S2) with different labeled (Y\({}^{1}\), Y\({}^{2}\)) and unlabeled views (X\({}_{1}\)\({}^{1}\), X\({}_{2}\)\({}^{1}\)) were hired for active learning (AL) and self-training (ST) respectively. The most informative examples predicted by classifier S1 whose confidence score is closest to 0.5 are selected by active learning for human annotation, more especially uncertainty sampling [70]. Further, the most confident level examples with threshold value 0.8 are selected by self-training. The final trained sentiment learner based on Active-Self-Co-Training achieved good

Figure 6.2 This figure shows the classification performance based on ST-SL with 4 different confidence threshold values on multi-domain datasets. performance on Book, DVD, Electronics and Kitchen domain dataset, and best performance on Movie domain dataset. Figure 12 shows the performance of the final sentiment learner based on ASCT on multi-domain datasets. **Experiment 6. **_Evaluation of final sentiment orientation based on classification fusion._

In this experiment, the predictions of final trained sentiment learners on test reviews are combined based on majority voting exploiting a threshold t (i.e., t \(\geq\) c, c = 3, where c is the value of threshold). In detail, the same sentiment orientation predicted by at least three sentiment learners is considered as the final sentiment polarity of the class. The approach of classification fusion based on majority voting for multi-domain sentiment classification achieved high performance.