Social media is one of the biggest forums to express opinions. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisal and emotions of people in regards to entities, events and their attributes. Sentiment analysis is also known as opinion mining. Opinion mining is to analyze and cluster the user generated data like reviews, blogs, comments, articles etc. These data find its way on social networking sites like twitter, facebook etc. Twitter has provided a very gigantic space for prediction of consumer brands, movie reviews, democratic electoral events, stock market, and popularity of celebrities.The main objective of opinion mining is to cluster the tweets into positive and negative clusters. An earlier work is based on supervised machine learning (Naïve bayes, maximum entropy classification and support vector machines). The proposed work is able to collect information from social networking sites like Twitter and the same is used for sentiment analysis. The processed meaningful tweets are cluster into two different clusters positive and negative using unsupervised machine learning technique such as spectral clustering. Manual analysis of such large number of tweets is impossible. So the automated approach of unsupervised learning as spectral clustering is used. The results are also visualized using scatter plot graph and hierarchical graph.
In this ongoing work, the location-aware ranking query (LRQ) are considered, an important category of location-aware query. Types of location-aware ranking query are the k-nearest neighbour (NN) query and location-aware keyword query(LKQ). NN LKQs and inquiries have vast applications in many domains. However, there are a great number of locationaware datasets that demand better and flexible location aware rank queries. They are a lot more complex than spatio-textual objects. These things are termed as location-aware things. For location-aware things, simple NN LKQs and queries may well not be expressive enough to find the objects of interests. In this particular proposed work the generic location-aware rank query is formulated, which retrieves the objects satisfying a query predicate, ranks and returns the full total results predicated on spatial proximity, textual relevance's and measures extracted from attribute values. We create a construction called location aware indexing and query processing(LINQ), for useful indexing and querying of GLRQs. LINQ evolves the synopses tree to work with synopses of non-spatial features, and combines the synopses tree with other indexes to query and index the GLRQ. The global buckets can be used to provide efficiency and faster computation time by using Bin sort algorithm this proposed method is recognized as STQP. The increased proposed system will provide better results with respect to faster and output for spatial query results.
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