# A Sentiment Analysis Model Based on User Experiences of Dubrovnik on the Tripadvisor Platform
## 2 Background and Related Work  
### Sentiment Analysis  
A sentiment analysis, also known as opinion mining, is an automated process that evaluates the sentiments expressed in a text in terms of positive, neutral, and negative opinions [16]. A fine-grained sentiment analysis includes the following categories: very positive, positive, neutral, negative, and very negative. These categories can be mapped to ratings, e.g., very positive can be mapped to a rating of 5. The term \"sentiment analysis\" was coined in 2001 in a study that attempted to determine and predict market sentiments on the basis of rating texts [17]. By the end of 2003, several studies using the same term had been published, contributing to its popularity. Although NLP has a long history, little research had been conducted on people's thoughts and feelings before 2000. This was partly because opinions in digital form were not widely available. Since then, sentiment analyses have become one of the most active areas of research (Google Trends, [[https://trends.google.com/trends/](https://trends.google.com/trends/)]([https://trends.google.com/trends/](https://trends.google.com/trends/)), accessed on 29 July 2024). A sentiment analysis can be conducted at various levels: Document, Sentence, and Aspect. Document level: evaluates the overall sentiment of a paragraph or document, assuming that it contains an opinion about a single entity, but does not support comparisons between multiple entities;  
Sentence level: Identifies the sentiment in each sentence and classifies it as subjective or objective and positive or negative. Since the sentences are shorter, techniques such as part-of-speech tagging (POS), parse trees, and lexicographic resources are used;  
Aspect level: Analyzes sentiments based on specific aspects or features of an entity (e.g., price, cleanliness, service, etc.). This involves identifying aspect terms, determining their polarity, identifying aspect categories, and analyzing sentiments in these categories [18]. The aforementioned levels of sentiment analyses serve as a basis for classification and are applied depending on the type of text and the domain analyzed. According to [19], there are three main approaches for a sentiment analysis: a lexicon-based approach, machine learning (ML), and a hybrid approach. The lexicon-based approach involves dictionary-based and corpus-based methods. In the first approach, a sentiment analysis is performed using dictionaries, such as Vader [20], SentiWordNet [21], SenticNet [22], etc., to assign sentiment scores to words, which are then aggregated to determine the overall sentiment. [PERSON] and [PERSON], in [23], used a sentiment analysis based on the Vader lexicon to identify differences between visitors of the Grand Canyon in the U.S. based on their reviews. The corpus-based approach, on the other hand, relies on statistical analysis using technologies based on K-Nearest Neighbors (KNNs), Conditional Random Fields (CRFs), and Hidden Markov Models (HMMs) instead of predefined dictionaries. The ML approach classifies sentiments based on statistical models. By training algorithms on large datasets, this model can learn how certain words express emotions and can take into account the emotional tone of other words and the frequency of their co-occurrence [19; 24]. ML approaches can be traditional (e.g., Naive Bayes, Support Vector Machine (SVM), Maximum Entropy, etc.) or deep learning (DL) models (e.g., Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Transformer Network, etc.). Although DL models often yield better results, they require extensive data and computing power. However, with the advent of cloud computing and powerful GPUs, the time required to train deep neural networks has decreased significantly [25; 26]. Both lexicon-based and ML approaches have their strengths and weaknesses, which has led to the development of hybrid methods that combine both to compensate for their respective limitations. For example, in [27], [PERSON] et al.