## 3 Proposed Models
### Base learners and Meta-Learner
The learners that contributed the most to the accuracy of the system were the full learner, the no-shift learner, and the deep learning learner, corresponding to no-shift features that did not cause sentiment shifts, and 'deep features'. The WLLR statistical model was not able to perform well with the Vietnamese language. The WLLR's classification of polarized words was insufficient, and data alone cannot increase the accuracy of polarized words. This approach revealed its drawbacks when dealing with grammar that had a complex semantic structure, such as Vietnamese. For instance, a well-known example error was classifying the word pair \"khong_thichon_t like\" and \"ghet_ha\" into the same ranking. However, in the

Figure 10: Results experimented on corpus Kitchen. English language, \"don't like\" may replace \"dislike.\" This can be solved by replacing the WLLR method with the emotional dictionary proposed by [PERSON] et al. [31] to rank pairs of words bearing sentiment. The reasons for our proposed method achieving acceptable results can be summarized as follows:

* Our model can capture various cases of sentiment shifts and introduce appropriate treatment for each. In improving on [PERSON] et al. 's approach, we have built additional base learners for various sub-datasets that improved system performance. * Our model uses deep learning to automatically learn features that are implicit as input to the meta-learner. * Our model proved the powerful ensemble learning suite by having datasets with features of different characteristics. ## 5 Conclusions and Future Research Plan

In this paper, we introduced a novel model that integrates the advantages of deep learning, machine learning, statistics, and rule-based techniques. Although the computational cost of the proposed system is higher than the compared algorithms, the system bears multiple distinctive characteristics. We combined different methods, identified polarity shifting based on language structures and techniques, and used a word-embedding model with deep learning. This approach

Figure 11: Average accuracy of the [PERSON] et al. approach. Figure 12: Average accuracy of CEM(6C-RL). captures both'surface features' and 'deep features' in text and allows our system to achieve results better than other models. In addition, experiments have demonstrated that the proposed model works effectively with other languages, such as English. Our future work will focus on analyzing and conducting more experiments on deep learning as well as polarity-shifting structures in texts with the objective of uncovering the limits (if that is the case) of existing models when applied to different datasets and domains in response to the sentiment classification of reviews on social networks (especially Twitter and Facebook), which presently capture the attention of the maximum population. Texts of this kind, which are often short and written using complex structures, alter the meanings of sentences and render their identification more difficult. One solution to this problem could involve broadening the rules and the vocabulary corpus to process these specific sentences. The intention to experiment with other deep learning models to strengthen the system and enhance its accuracy will be taken account of. As mentioned in the evaluation, replacing the WLLR statistical method with the VNSD dictionary [31] was considered for sentiment score ranking. Writing--original draft, T.K.T. ; Writing--review & editing, T.T.P. This research is funded by Ho Chi Minh City University of Technology, VNU-HCM, under grant number BK-SDH-2019-8141216. The authors declare no conflict of interest. ## References

* [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] Sentiment analysis based on clustering: A framework in improving accuracy and recognizing neutral opinions. _Appl. Intell._**2014**, _40_, 441-452. [CrossRef]
* [PERSON] et al. (2003) [PERSON]; [PERSON]; [PERSON] Mining the peanut gallery: opinion extraction and semantic classification of product reviews.