# An Effective ELECTRA-Based Pipeline for Sentiment Analysis of Tourist Attraction Reviews
## 4 Experimental Design  
### Dataset  
There is a large number of datasets publicly available to facilitate the study of sentiment analysis in recent years. Most of these datasets are movie or product reviews, such as from Yelp ([[https://www.kaggle.com/yelp-dataset/yelp-dataset](https://www.kaggle.com/yelp-dataset/yelp-dataset)]([https://www.kaggle.com/yelp-dataset/yelp-dataset](https://www.kaggle.com/yelp-dataset/yelp-dataset)), accessed on 16 July 2022), Amazon ([[https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products](https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products)]([https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products](https://www.kaggle.com/datafiniti/consumer-reviews-of-amazon-products)), accessed on 16 July 2022), IMDb ([[https://www.kaggle.com/lakshmi25](https://www.kaggle.com/lakshmi25) npathi/imdb-data]([https://www.kaggle.com/lakshmi25](https://www.kaggle.com/lakshmi25) npathi/imdb-data) set-of-50k-movie-reviews, accessed on 16 July 2022) and SST ([[https://www.kaggle.com/a](https://www.kaggle.com/a)]([https://www.kaggle.com/a](https://www.kaggle.com/a)) tulanandjha/stanford-sentiment-treebank-v2-sst2, accessed on 16 July 2022). The tourism-related dataset only covers restaurants or hotels, mostly in English, such as SemEval2014-ABSA-Restaurant-Reviews ([[https://alt.qcri.org/semeval2014/task4](https://alt.qcri.org/semeval2014/task4)]([https://alt.qcri.org/semeval2014/task4](https://alt.qcri.org/semeval2014/task4)), accessed on 16 July 2022) and ChusentiCorp-ht ([[https://raw.githubusercontent.com/SophonPlus/ChineseNI](https://raw.githubusercontent.com/SophonPlus/ChineseNI)]([https://raw.githubusercontent.com/SophonPlus/ChineseNI](https://raw.githubusercontent.com/SophonPlus/ChineseNI)) pCorpus, accessed on 16 July 2022). Therefore, we have constructed a sentiment dataset of tourist attraction reviews in Chinese from Ctrip. Our dataset is called SenTARev (Sentiment analysis for Tourist Attraction Reviews). In the adopted labeling scheme, there are two types of labels, positive (PL) and negative (NL). There are three types of affective outcomes, negative (Neg), positive (Pos) and neutral (Neu). Considering labeling as a classification task, it is defined as follows: if positive sentiment is detected, PL outputs a value of 1 as a positive class and 0 otherwise; if negative sentiment is detected, NL outputs a value of 1 as a negative class and 0 otherwise. Thus, for PL, a value of 1 means a positive or neutral sentiment and a value of 0 means a neutral or negative sentiment. For NL, the value 1 implies a negative or neutral sentiment and the value 0 implies a neutral or positive sentiment. We organize annotators to perform polarity annotation and data inspection on the raw data, which is finally used as a supervised corpus for fine-tuning the ELECTRA model. In the quality-control process of human-annotated data, each attraction was assigned an annotator and an inspector. We set a unified annotation specification before annotation to ensure the consistency of the data. Every annotator had to perform real-time inspection, and every inspector had to complete full sample inspection and sampling inspection. Table 1 illustrates our annotation scheme and SenTARev's label distribution.