Many consumers are currently interacting via social media. The social media sites form platforms from which the consumers express themselves and give relevant feedback regarding their experiences with various brands, products, and services. The data about such interactions could be gained from social media sites such as Facebook and Twitter, translating into sentiments that could be analyzed to predict consumer preferences and trends in their needs. With an increase in the volume of data about these interactions, the concept of big data has evolved and aids in achieving big data analytics for sentiment analysis and trend prediction. In this study, a web mining approach has been employed due to a lack of authentic weblog information. The aim is to get important data regarding the behavior of tourists and their feedback regarding various sites of attraction in the context of Oman. Some of the data collection tools that have been utilized include rapid mine operators and Apache Flume to gain information from Facebook and Twitter. To establish an adequate data mining framework, the study has employed CRISP-DM, eventually achieving various patterns regarding heritage tourism in Oman. To analyze data from Twitter, the study has relied on Apache Hive and Apache Hadoop.

Many consumers are currently interacting via social media. The social media sites form platforms from which the consumers express themselves and give relevant feedback regarding their experiences with various brands, products, and services. The data about such interactions could be gained from socia...