## 4 LeSSA--Implementation and Technical Details
### Linguistic Rules
#### 4.2.6 Active Self-Training Based Sentiment Learner
The classification performance obtained by CF is shown in Figure 6.3

Figure 6.4 This figure shows the classification performance achieved by AST-SL on multi-domain datasets. Figure 6.5 This figure shows the classification performance achieved by ASCT-SL on multi-domain test datasets. ### Result Discussion

The performance of all the designed and implemented sentiment analysis techniques on multi-domain datasets is shown in Figure 14 along with the Table. As shown in Figure 14, the WCSL based sentiment classification obtained the best performance on Kitchen review dataset; the HRPL-SL achieved the best performance on Books and DVD datasets; and the AACT-SL achieved the best performance on Electronics and Movie review datasets, while the performances of ST-SL and AST-SL on multiple domain datasets were satisfactory. Further, the CF-based on simple majority voting improved the classification performance. In this approach (CF), we combined the classification predictions from all the sentiment learners on test datasets using majority voting and determined the final sentiment orientation for each review text in the test datasets. Figure 6.6 This figure shows the classification performance achieved by different designed sentiment classification models. Figure 6.7 This figure shows the classification performance achieved by CF on multi-domain test datasets. ### Performance Comparison with State-of-the-Art Methods

In order to show the effectiveness of our proposed approach, we compared it with the following state-of-the-art methods for textual sentiment classification. **Active deep network (ADN)**[41]: ADN is a semi-supervised learning method which exploits the embedding information from the huge amount of unlabeled data together with a small number of labeled data for review sentiment classification. **Dual-view co-training**[31]: A dual-view co-training method, which makes use of the original and anonymous views in pairs, in the training, bootstrapping and testing process, all based on a joint observation of two views for semi-supervised sentiment classification. **Self-training-S**[61]: A self-training approach in which multiple feature subspace-based classifiers are used to explore a set of good features and select informative samples for automatic labeling. In this approach, the top two informative samples are selected for manual annotation in each iteration. **LCCT**[37]: A semi-supervised model based on lexicon and corpus-based co-training for sentiment classification. **CAST**[29]: A co-operative hybrid semi-supervised learning for text sentiment classification

Figure 15 along with table shows sentiment classification performance of LESSA against state-of-the-art approaches. It is clear that our proposed approach LESSA achieved the highest performance in terms of accuracy on the multi-domain datasets. There are many reasons for the high performance of the proposed approach as compared to other approaches. The first reason for achieving the highest performance of the proposed SA framework is the construction of high-quality wide-coverage sentiment lexicons, leveraging linguistic and semantic knowledge. The second reason is the selection of most representative instances for the initial training set. The third reason is training high-quality data with semi-supervised techniques, considering both the most informative and high-confident examples. The fourth reason is the removal of noisy and redundant features from the text data during pre-processing. The fifth reason is the extraction and selection of sentiment bearing words from textual data in each iterative round during sentiment classifier learning. The sixth reason is the prediction of final sentiment orientation based on classification fusion. ## 6 Conclusions

A massive amount of unstructured textual data in the form of users' reviews is available online. This unstructured data contains beneficial information for business analysis and decision making. SA is

Figure 6.8 Comparison of LESSA-CF with state-of-the-art approaches for textual sentiment classification.