## 4 LeSSA--Implementation and Technical Details
### Linguistic Rules
#### 4.2.6 Active Self-Training Based Sentiment Learner
## 5 Results, Evaluation and Discussion

### Datasets

We evaluate our proposed unified framework on the two different domain datasets, the Cornell movie review dataset [7] and Amazon product review datasets [66]. The Cornell movie review dataset

Figure 7: Prediction of final sentiment polarity based on classification fusion. consists of 2000 reviews contain 1000 positive and 1000 negative reviews. The Amazon product reviews datasets contain four domains, i.e., Book, DVD, Electronics, and Kitchen; each domain consists of 1000 positive and 1000 negative reviews. For each domain dataset, we used 80 percent instances as the training set and the remaining 20 percent instances as the test set. We select 10 percent instances of the training set as the initial labeled data with reinforcement of the labeled data using the SBC technique and used the remaining instances as the unlabeled data. The classification performance of final trained sentiment learners is evaluated on test documents. ### Experimental Setting

Similar to state-of-the-art techniques for multi-domain datasets, we used 5-fold cross-validation for all the experiments in multi-domain data sets. Each dataset was split randomly into a training set and testing set in 5-fold cross-validation, one of which is used for testing, and the remaining including initial set are used for training. The classification performance is estimated by the averaged results of 5-fold cross-validation. For the implementation, we exploited Rapidminer Studio [67], which provides a complete environment for machine learning, text mining, deep learning, data processing and predictive analytics. We used SVM (Linear) for HRPL-SL. Multinomial Naive Bayes (MNBs) is utilized as the base classifier of ST-SL, AST-SL, AST-SL [68; 69] with all parameters set at their default values. SVM (Linear) is also used for the final trained sentiment learners to classify test textual reviews. We set the thresholds of text samples as p = 3, n = 2 (p = positive class, n = negative class) for ST-SL, AST-SL, AST-SL. The total number of iterations is set to 90 for Algorithm 2, 3 and 4. Sentiment features were extracted and selected on the training dataset by using MRMR selection method. The dimension for appropriate sentiment feature is 800. We utilized the paired t-test (P < 0.05) to calculate the accuracies of all techniques. ### Evaluation Metrics

We used accuracy as an evaluation criterion to calculate the overall sentiment classification performance. The classification accuracy of a method is measured based on the testing texts. \[\text{Accuracy }=\text{ correctly classified reviews/total number of review} \tag{2}\]

### Experimental Results and Discussion

In this section, we evaluate and show the effects of various parts of the proposed approach on multi-domain datasets. We also evaluate and discuss how the proposed approach could overcome the sentiment analysis problem. **Experiment 1. **_Evaluation of induced WCSL for review document scoring and classification._

The induced high-quality WCSL in conjunction with linguistic and semantic knowledge achieved good performance on Book, DVD, Electronics and Kitchen datasets and best classification performance on Movie domain dataset. The effective performance of induced high-quality WCSL is due to the wide coverage of general sentiment words including context-aware domain-specific sentiment words which matched many sentiment words in the review documents. The classification accuracy achieved by WCSL on multi-domain datasets is shown in Figure 8.

**Experiment 2. **_Evaluation of high ranking pseudo-labeled examples for review sentiment classification._

The selection of high ranking pseudo-labeled example by WCSL for sentiment classification achieved good performance on Book, DVD and Electronics domain datasets and best performance on Kitchen and Movie domains. The influential performance of this approach is due to the selection of high absolute score sentiment examples, which comprise effective sentiment words.