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Yayın Tweet sentiment analysis for cryptocurrencies(IEEE, 2021-10-13) Şaşmaz, Emre; Tek, Faik BorayMany traders believe in and use Twitter tweets to guide their daily cryptocurrency trading. In this project, we investigated the feasibility of automated sentiment analysis for cryptocurrencies. For the study, we targeted one cryptocurrency (NEO) altcoin and collected related data. The data collection and cleaning were essential components of the study. First, the last five years of daily tweets with NEO hashtags were obtained from Twitter. The collected tweets were then filtered to contain or mention only NEO. We manually tagged a subset of the tweets with positive, negative, and neutral sentiment labels. We trained and tested a Random Forest classifier on the labeled data where the test set accuracy reached 77%. In the second phase of the study, we investigated whether the daily sentiment of the tweets was correlated with the NEO price. We found positive correlations between the number of tweets and the daily prices, and between the prices of different crypto coins. We share the data publicly.Yayın Leveraging transformer-based language models for enhanced service insight in tourism(IEEE, 2023-12-22) Er, Aleyna; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanCustomer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning.
