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These days' people like to explore numerous places all over the world predominantly which are highly recommended. For instance, people who are new to a particular location regularly choose places to visit manually by typing some of them wished by the user working on search engine applications. It becomes difficult whenever to search manually and plan accordingly which might be not accurate. To overcome the above issues, the travel recommendation system applies sentiment analysis comparing user preferences, number of days, number of people and delivers the top recommended places to the user considering the pre-existing reviews of the places which are recommended. While processing it calculates the similarity between user preferences/inclinations and reviews using sentimental attributes (positive and negative) to match the similarity. Travel recommendation system takes sentiment polarity and similarity value as parameters on the whole, also the days to be visited and suggests esteem places.

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Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about “events,” and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. Online content exhibits rich temporal dynamics, and diverse real-time user generated content further intensifies this process. However, temporal patterns by which online content grows and fades over time, and by which different pieces of content compete for attention remain largely unexplored. This article describes tracking and analyzing public sentiment on social networks and finding the possible reasons causing these variations. It is important to find the decision from public views and opinion in different domain. They can be used to discover special topics or aspects in one text collection in comparison with another background text collection. The implemented method attains the 95% accuracy while predict the sentiments from the social websites and the 96.3% of the opinion rate with minimum time.