# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 1 Introduction
The main scientific problem we solve in this paper is answering the question of how to identify the authenticity and utility of the extracted tweets as equivalents of online reviews. Various types of approaches were developed and improved to tackle the task of extracting valuable information from the Internet by proposing POI recommendations that provide a location suitable as per user's preferences. Some of the most successful approaches so far include rule-based or statistical approaches, while novel Deep Learning-based approaches are yet to be commonly used ([PERSON] et al. [7]). Figure 1: A map and a bird’s eye view on the target sightseeing spots analyzed in this study—Ngornogoro and Serengeti NP. This study attempts to address the task of obtaining online reviews (UGC data source category) by extracting Twitter microblog posts (tweets), in form of textual data with the aim to extract useful information and further create a classifier to determine whether the tweets are likely to carry similar information. This task is widely recognized as text classification which is one of the fundamental tasks in Natural Language Processing (NLP). Due to its nature, text classification has important implications for NLP tasks, which aim to either analyze, understand, or produce human language. Text classification has a large potential for various applications in the domain of text mining, especially those that require semantic analysis, such as author profiling and sentiment analysis ([PERSON] et al. [8]). Categorizing tweets has been challenging due to insufficient contextual information and noisy possession. Recently, [PERSON] et al. [9] suggested a disaster management multi model approach for identifying actionable information from disaster-related tweets using Bert, graph attention network and relation network.In their work, the focus is on multiple classification so as to allow rapid detection of various categories of tweets. Their approaches outperform state-of-the-art approaches. [PERSON] et al. [10] proposed a real-time analysis method of detecting tourists spots from geotagged tweets using location information from tweets and a time-series changes. Their method revealed improvements compared to their previous moving-average method. Compared to above related works, we use BERT for both binary text classification (on-spot tweets or not) and multi text classification where we identify the semantic polarity of the tweets using a three and five stage rating score. The main contribution of this study is four-fold. Firstly, this study proposes a mining framework that cultivates on-spot reviews and related POI from geotagged tweets by using location clustering and BERT neural network model. Secondly, it adds most probable rating score to the on-spot reviews extracted by learning the sentiment orientation of the tweets using BERT neural network model. Thirdly, we develop a corpus of on-spot annotated tweets which can further be applicable in other NLP tasks. Finally, we designed a web system (PSRS) and use selected and rated tweets as touristic information. In a global perspective, this study intends to support the local tourism sector in Tanzania specifically in the area of wildlife-based tourism as one of the promising and fastest-growing sector among others in Tanzania, with the selected target spot attracting the most sightseers [11; 12; 13]. The rest of this paper is organized as follows. Section 2 introduces related works and previous research. Section 3 briefly outlines the proposed method used in this research. Section 4 describes the applied data. Experiment setup and analysis of the results are discussed in Section 5. Additionally, Section 6 discusses results and various experimental findings. Section 7 introduces the designed system (PSRS). Finally, in Section 8 we present conclusions and future works.