The paper aims to identify unique features in the image of four Baltic cities: Gdansk, Kaliningrad, Riga, and Szczecin, based on an analysis of reviews posted on the TripAdvisor portal. The text mining technique was used to extract the words most frequently used in opinions, while sentiment analysis was performed to assess the strength of negative and positive reviews. Analysis of variance was used to extract the unique and common features of the image of each city analysed. The results showed that Riga and Gdansk have the largest number of unique features/attributes, while Kaliningrad has the smallest. Positive and negative sentiment analysis indicated that Gdansk and Szczecin have a higher proportion of positive sentiment in reviews than Riga and Kaliningrad. The study confirmed the importance of traveller-generated content as an image-building agent, and shows that destination image attributes can be effectively identified using text mining in both the cognitive and affective dimensions. It also showed that it is possible to identify significant differences in the image of a destination, which can subsequently be used by DMOs in the branding process to distinguish destinations from one another on the tourism market.

The aim of this article is to analyse the experiences gained by tourists visiting one of the most visited protected areas in Poland—the Tatra National Park (TNP). The authors focused on the following question: does the natural heritage of the national park affect visitors’ unique experiences or is environmentally valuable area not important for their experiences? This article uses mixed quantitative (Text Mining, co-occurrence network analysis) and qualitative (narratives research) methods. Data for analysis—revives posted by users between April 2011 and September 2019—were downloaded from TripAdvisor.co.uk. Reviews on TripAdvisor indicate that the most important for visiting tourists were the experiences of physical activity. This confirms the trend of maintaining health and the desire to regenerate physical strength. The group of reviews related to connection to nature experiences is extremely small, which indicates that tourists probably did not come to TNP as a result of a preference for experiences related to ecological awareness. Some tourists felt tension, which indicates that the carrying capacity was exceeded. There is a doubt as to whether tourists who want to engage in physical activity must necessarily visit the area with the highest degree of nature protection.

The aim of the article is to analyze the relationship between lifestyle changes and willingness to use sharing economy services in tourism, including peer-to-peer accommodation. On the one hand, knowledge of lifestyle changes can help adapt the product offer to the requirements of consumers. On the other hand, products that consumers use can reflect lifestyle changes. The following classification of motivations for sharing economy activity selection resulting from the subjects’ lifestyles has been proposed: personal motivations—related to economic advantages; social (conformist) motivations—resulting from the need to fit in with others; and ideological motivation—resulting from the understanding of the processes of natural environment degradation and excessive consumption. In order to gather opinions and to understand behaviors, attitudes, and preferences regarding sharing economy activities (i.e., the sharing of transportation, food, clothes, equipment, and accommodation), the focus group interview method was used (6 groups, 5–8 participants each). Discussions were conducted separately for two populations: young with time (YT) and older rich (OR). The study demonstrates lifestyle changes between the generations. YT actions are the consequence of personal and ideological motivations. OR have lifestyles that result from personal and conformist motivations. Neither population sees a relationship between participating in the sharing economy and caring for the environment and preventing excessive consumption.

The article aims to analyse and compare experiences gained by tourists visiting three national parks in Poland. The authors focused on the following questions: What are people's experiences visiting national parks in Poland? Do the natural assets of the national parks affect visitors' unique experiences, or are environmentally valuable areas not crucial for their experiences? The authors used mixed quantitative (text mining, correspondence analysis) and qualitative (content analysis) methods. The data for analysis were opinions written by TripAdvisor users. Reviews on TripAdvisor indicate that the most important experiences for tourists in the National Parks studied were Nature appreciation and Physical activity. The other groups of experiences reflected in the reviews were: Aesthetic, Connection, Tension and Excitement. This confirms that nature is the most important feature of national parks for tourists, but it also indicates a trend to maintain good health and the desire to regenerate physical strength in areas of natural beauty.

The article aims to identify memorable gastronomic experiences reported online and verify their relationships with the type of cuisine served and restaurant location. This study used text mining, LDA, Pearson’s chi-squaredtest and sentiment analysis. All 48,378 English reviews posted by TripAdvisor users concerning 155 restaurants in Krakow were scraped. Eight features that characterise MGEs were identified (service/staff, atmosphere,cuisine/food (taste), drinks, local specialities, location/setting, price & value and table booking). There are statistically significant differences in the frequency of the topic experiences depending on the location of restaurants in the city.

This article aims to (1) identify guests’ memorable experiences based on reviews posted on TripAdvisor, (2) identify the differences in memorable experiences due to hotel location, evaluation on TripAdvisor and consumer sentiment. The study used quantitative methods: text mining, topic modelling, and sentiment analysis. All reviews (n = 34,992) for all Warsaw hotels included on TripAdvisor (N = 99) were analysed. Seven topics of memorable experiences were identified via Latent Dirichlet Allocation analysis: five were very positive and two very negative. The content analysis of those topics allowed us to extract six positive factors which include: (1) view from the floor, (2) hotel staff and service, (3) breakfast and restaurant, (4) location and atmosphere, (5) pool, lounge, gym and spa, (6) price (value for money), as well as two negative factors: (1) noise at night, (2) booking and check-in. In addition, it was found that memorable hotel experiences differ depending on the location of the hotel in the city.