Sentiment classification from reviews for tourism analytics

User-generated content is critical for tourism destination management as it could help them identify their customers' opinions and come up with solutions to upgrade their tourism organizations as it could help them identify customer opinions. There are many reviews on social media and it is difficult for these organizations to analyse the reviews manually. By applying sentiment classification, reviews can be classified into several classes and help ease decision-making. The reviews contain noisy contents, such as typos and emoticons, which could affect the accuracy of the classifiers. This study evaluates the reviews using Support Vector Machine and Random Forest models to identify a suitable classifier. The main phases in this study are data collection, data preparation, data labelling and modelling phases. The reviews are labelled into three sentiments; positive, neutral, and negative. During pre-processing, steps such as removing the missing value, tokenization, case folding, stop words removal, stemming, and applying n-grams are performed. The result of this research is evaluated by looking at the performance of the models based on accuracy where the result with the highest accuracy is chosen as the solution. In this study, data is data from TripAdvisor and Google reviews using web scraping tools. The findings show that the Support Vector Machine model with 5-fold cross-validation the most suitable classifier with an accuracy of 67.97% compared to Naive Bayes with 61.33% accuracy and Random Forest classifier with 63.55% accuracy. In conclusion, the result of this paper could provide important information in tourism besides determining the suitable algorithm to be used for Sentiment Analysis related to the tourism domain

Assessing COVID-19 impact on user opinion towards videogames - Sentiment analysis and structural break detection on steam data

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAs we live in a world where the videogame industry grows day by day and new media is constantly emerging, user feedback can be widely found online. User reviews are a highly valuable data source when studying player perception of a videogame. They are also apparently volatile to updates released by developers and other external events, which may change user opinion over time. Here we assess whether the COVID-19 pandemic outbreak fell in this category, having or not a noticeable impact on the player view and popularity of videogames. In this research, we build and implement a method to collect active player data and user reviews, identifying the sentiment contained in the expressed opinions. Furthermore, we investigate the existence of structural breaks in the time series we target. For this purpose, we targeted user-reviews and active player data collected of Steam’s twenty most popular Massive Multiplayer Online Role- Playing Games. To collect sentiment polarity values, two Natural Language Processing Python libraries were used, TextBlob and VADER, and structural break detection was put into practice using strucchange R package. The results of this work show us that despite having a great effect on the number of active players, the COVID-19 pandemic did not produce the same impact on Steam user reviews. Nonetheless, we were able to identify one of the platform’s major reviewing related updates as a structural break. We believe this approach can be used for further assessments on public opinion towards a specific product, in the future