Sentiment Analysis of News Event-based Social Network using Data Mining Technique

The increasing popularity of social media takes the attention of the internet users across the word wide to discuss and share the events/things they are interested on social media blogs/sites. Consequently, an explosive increase of social media data spread on the web has been promoting the development of analysis of social media news depending on the news or events, the latest trend of the social big data. The sentiment analysis of news event becomes an important research area for many real-world applications, such as public opinion monitoring for government and news recommendation of news websites.  In this paper, we perform sentiment analysis for news events based on posts, and comments of the users upon a news event. We use two data mining techniques namely naïve Bayesian and support vector machine to reveal what the polarity/meaning of the post is such as positive, negative or polarity. There are two main stages in performing this task called training and testing phases. The first phase uses the training datasets of the news event and the second phase use newly inputted data of the user to classify the polarity of the user news posts or comments. We then execute the experiments for each algorithm and then collect the experimental results and compare them with accuracy with known and unknown test data with different volumes of tweet transactions. According to the results, both of them can accurately reveal the opinions of the social network users

Uniformly Integrated Database Approach for Heterogenous Databases

The demands of more storage, scalability, commodity of heterogenous data for storing, analyzing and retrieving data are rapidly increasing in today data-centric area such as cloud computing, big data analytics, etc. These demands cannot be solely handled by relational database system (RDBMS) due to its strict relational model for scalability and adaptability. Therefore, NoSQL (Not only SQL) database called non-relational database is recently introduced to extend RDBMS, and now it is widely used in some software developments. As a result, it becomes challenges regarding how to transform relational to non-relational database or how to integrate them to achieve business purposes regarding storage and adaptability. This paper therefore proposes an approach for uniformly integrated database to integrate data separately extracted from individual database schema from relational and NoSQL database systems. We firstly try to map the data elements in terms of their semantic meaning and structures with the help of ontological semantic mapping and metamodeling from the extracted data. We then cover structural, semantical and syntactical diversity of each database schema and produce integrated database results. To prove efficiency and usefulness of our proposed system, we test our developed system with popular datasets in BSON and traditional sql format using MongoDB and MySQL database. According to the results compared with other proficient contemporary approaches, we have achieved significant results in mapping similarity results although running time and retrieval time are competitive with the others