## 4 Performance Evaluation of the Proposed Framework
### Sentiment Analysis of Big Data  
This subsection presents a use-case scenario of real-time sentiment analysis on Twitter data of several universities collected using Twitter app. The main idea is to analyse thousands of tweets coming at every second within a very short amount of time. The framework should be dependent on the imported data volume. This is important because the volume of tweets is growing at a noticeable rate and the cluster size needs to scale up based on the data size. In this real-time study, the Twitter data was collected for sentiment analysis in the form of a live stream, from eight UK Universities, including: Brunel University, Imperial College London, Kingston University, Queen Mary University of London, Liverpool University, Bedfordshire University, Middlesex University and Cambridge University. To analyse the streaming data at a high data rate, the data is processed using the proposed CEAS framework with auto-scaling cluster on Hadoop and MapReduce is employed to perform the sentiment analysis on the universities' Twitter data. Over 200 million twitter messages collected from the above mentioned universities are used to identify and classify the feedback given to the universities. Twitter's timeline APIs are exploited to retrieve the universities' data. Here, the tweets contain the information about that specific Tweet and the name of the user who tweeted on the social web. Specifically, the raw json tweet mainly contains the following fields: (1) Identification of the Tweet by Tweet ID, (2) user names associated with Tweets, and (3) the text of the tweet and its timestamp. The Tweets are classified into: _positives_, _negatives_ and _neutral tweets_. As a means to perceive and differentiate user opinions, the sentiments should be detected in the post. Examples are given below for positive and negative tweets with a notice that they may reflect completely different opinions:  
* _Example 1_: \"text\":\"@georgierdarling Hi @georgierdarling I would be happy to help! I am a voice coach and senior lecturer in voice at Brunel Uni
* _Example 2_: \"text\":\"RT @[PERSON]_kelly96: It is extremely disappointing to see that Brunel university have members of staff working as the voice of the students and collected tweets from Rest API by using the Twitter4j library. There exist many challenges in the sentiment analysis. For instance, in the above examples, the user is expressing his own interest towards his role in the first example. However, an opinion word which is considered to be positive in one state may be seen as negative in a different situation. This is due to the fact that people may not always express the same views. This indeed can be noticed in the second example in which the user's opinion is different and tweeted against the first one. In general, both information can be used even if these are contradicting each other. The sentiment analysis of Twitter data is realised as follows: in the preprocessing phase, the tweets are available as raw text data. The punctuation and other symbols can be removed from the tweets as it might affect the accuracy of the overall evaluation process. The tweets are labeled based on University names. This data is then processed using the proposed CEAS approach with auto-scaling cluster configuration of the Hadoop framework as follows: At first, Mapper takes tweets from the universities json and calculates the weights on each tweet by using the weights dictionary and categorises the university names and weights into the output as a key-value pair. Then, Reducer takes the outputs from the Mapper and aggregates all the tweets for each Mapper's outputs. Figure 6 shows the aggregate results obtained from the Universities' tweets data. These results can be used to study the user's opinion about the Universities and also to compare these opinions between different Universities which ultimately helps the user in making a good University selection. The aggregated results of the tasks are performed on the auto-scaling Hadoop cluster for the sentiment analysis.