## 4 LeSSA--Implementation and Technical Details
### Linguistic Rules
#### 4.2.6 Active Self-Training Based Sentiment Learner
an influential research area, which aims to classify online user's reviews into positive and negative classes. In this context, diverse techniques such as SML, SSL and LS for textual sentiment classification have been proposed. The main difficulty in SML approach is the absence of enough labeled training data for heterogeneous domains. SSL approaches for textual sentiment classification have improved their performance while suffering from research challenges, whereas, most of the SSL approaches could not identify efficaciously important concealed information in the unstructured data. Moreover, the most significant sentiment feature extraction and selection are not handled successfully. Noisy and redundant features are also ignored in the textual data. Many of them randomly selected initial training set for first classifier training. Besides, the lexicon-based approaches for textual sentiment classification need improvement. In order to tackle and investigate such concerns, we propose a novel unified sentiment analysis framework, which we call \"LeSSA\" for textual sentiment classification. LeSSA is a novel framework, which has been designed for multi-domain sentiment classification. LeSSA is applicable to heterogeneous domain sentiment classification. LeSSA predicts the accurate sentiment label for the unseen textual reviews. A number of experiments were performed to validate the usefulness of LeSSA. The experimental results on multi-domain benchmark datasets show that LeSSA outperforms existing SSL, lexicon-based and hybrid approaches. In future work, we will incorporate other techniques, such as deep learning and linear discriminate analysis for testing LeSSA. [PERSON]-[PERSON] provided supervision, remarks and financial support for the paper; [PERSON] presented the idea; devised, designed and accomplished all the experiments and wrote the paper. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2019-2015-0-00742) supervised by the IITP (Institute for Information and communications Technology Planning and Evaluation). The authors declare no conflict of interest. ## References

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