This research has been executed into two similar verticals to extract the best possible accurate result, first vertical has been utilized Hadoop ecosystem to perform natural text processing on tweeter data to conclude the opinion about Oman traffic another vertical has been utilized the RapidMiner capabilities to perform web mining to get insights about Oman traffic. Web mining has been chosen alternatively instead of web log analytics due to unavailability of the web log data. Oman Traffic Sentiment Analysis by using Big Data Analytics project will collect many tweets on Twitter. This project use twitter data for social purpose according to our data requirement and processing the data. Different organizations are using this huge structured and unstructured data for extracting the people's views towards their industrial and business purpose for growing the company. We are taking the same idea to analyze the big data for the betterment of Oman traffic and accident. Oman Traffic Sentiment Analysis by using Big Data Analytics project will take Tweets for people opinion or feedback about Oman traffic as input. Then, Twitter will store tweets in JSON format. Then, Tweets will be collect, aggregate and moving using Flume into Hadoop in which pre-processing is done. After that, use Hive to classify data into positive and for negative opinions generating report. Finally, report generated from Hive output. The intended user will be a member of the public who is interested in the sentiment of the Twitter population with respect to traffic in Oman topic. Users are not expected to have a very high level of technical expertise. In addition, user should be familiar with using social media program such as twitter.

This research has been executed into two similar verticals to extract the best possible accurate result, first vertical has been utilized Hadoop ecosystem to perform natural text processing on tweeter data to conclude the opinion about Oman traffic another vertical has been utilized the RapidMiner c...