# An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews
## 5 Conclusions  
In this paper, we set out to improve sentiment analysis in tourist review data. Firstly, we constructed a Chinese tourist attraction review dataset, SenTAev, from Ctrip. Then, we proposed a two-step pipeline approach for the sentiment analysis of tourist attraction reviews. We found that an ELECTRA-based pipeline model is highly efficient at the sentiment analysis of tourist attraction reviews in terms of model performance. This represented, on average, a 6.33 percent improvement in \(m\_avg_{F}\) and averaged a 1.5 percent improvement in \(w\_avg_{F}\). In addition, we also found that pre-processing can further enhance pre-trained sentiment classification models, especially negation substitution. Our work can expand and promote the development of sentiment analysis of other domains. In the future, we will explore pre-trained models with travel knowledge enhancement to improve their ability to understand and represent domain knowledge. Furthermore, we will integrate pre-processing with deep learning models through transformation and filtering with the chi-quared method to further improve the efficiency of Chinese sentiment analysis. Conceptualization, H.F. and G.X. ; methodology, H.F.; software, W.T. ; validation, H.F., Y.L. and W.T. ; formal analysis, H.F.; investigation, H.F.; resources, G.X. ; data curation, W.T. ; writing--original draft preparation, H.F.; writing--review and editing, Y.L. ; visualization, W.T. ; supervision, G.X. ; project administration, G.X. ; funding acquisition, G.X. All authors have read and agreed to the published version of the manuscript. This research was funded by Central Leading Local Project \"Fujian Mental Health Human-Computer Interaction Technology Research Center\" (grant number 2020I.3024). Not acceptable. The GitHub address for the SenTARev dataset is [[https://github.com/peterpan23/SenTARev-](https://github.com/peterpan23/SenTARev-)]([https://github.com/peterpan23/SenTARev-](https://github.com/peterpan23/SenTARev-)), accessed on 11 August 2022. The authors express our acknowledgement to my colleagues in Minjiang University and Assistant Professor [PERSON] in University of Essex for their valuable suggestions. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & \(F_{\text{New}}\) & \(F_{\text{Neg}}\) & \(F_{Pos}\) & \(m\_avg_{F}\) & \(w\_avg_{F}\) \\ \hline None & 0.4954 & 0.8883 & 0.8029 & 0.8022 & 0.9425 \\ Stop & 0.5000 & 0.8856 & 0.9145 & 0.8041 & 0.9463 \\ Symbol & 0.5158 & 0.8857 & 0.9269 & 0.8065 & 0.9475 \\ Deletion & 0.5118 & 0.8882 & 0.9274 & 0.8058 & 0.9488 \\ Negation & 0.5294 & 0.8833 & 0.9387 & 0.8108 & 0.9501 \\ Stop + Symbol & 0.5326 & 0.8850 & 0.9464 & 0.8110 & 0.9521 \\ Deletion + Symbol & 0.5405 & 0.8876 & 0.9498 & 0.8120 & 0.9513 \\ Deletion + Stop & 0.5422 & 0.8875 & 0.9421 & 0.8102 & 0.9502 \\ Symbol + Negation & 0.5569 & 0.8964 & 0.9486 & 0.8140 & 0.9543 \\ Deletion + Negation & 0.5589 & 0.8977 & 0.9462 & 0.8143 & 0.9573 \\ Stop + Negation & 0.5400 & 0.8947 & 0.9438 & 0.8122 & 0.9548 \\ Deletion + Symbol + Negation & 0.5799 & 0.8982 & 0.9848 & 0.8232 & 0.9646 \\ Deletion + Symbol + Stop & 0.5521 & 0.8967 & 0.9768 & 0.8215 & 0.9619 \\ Deletion + Stop + Negation & 0.5755 & 0.8943 & 0.9744 & 0.8207 & 0.9622 \\ Stop + Symbol + Negation & 0.5736 & 0.8941 & 0.9713 & 0.8218 & 0.9638 \\ ALL & 0.5818 * & 0.9062 * & 0.9864 * & 0.8246 * & 0.9663 * \\ \hline \hline \end{tabular}  
* Denotes significance at \(p\leq 0.05\).  
\end{table} 
Table 3: Ablation experimental results on performance comparison. * _[PERSON] and [PERSON] (2018)_ [PERSON]; [PERSON] Chinese text sentiment classification by H-RNN-CNN. _Beijing Da Xue Xue Bao_**2018**, _54_, 459-465. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Aspect-based sentiment analysis with alternating coattention networks. _Inf. Process.