Analysis of Twitter Specific Preprocessing Technique for Tweets

Analysis of tweets in Twitter

Twitter is a microblogging service that commands more than 288 million monthly active users (in 2015) and is growing fast. Twitter users post short messages called tweet about any topic and follow others to receive their tweets. The great amount of data coming from Twitter users is a meaning source of information regarding different aspects of people life. The goal of this paper is to mine such...

2016 Olympic Games on Twitter: Sentiment Analysis of Sports Fans Tweets using Big Data Framework

Big data analytics is one of the most important subjects in computer science. Today, due to the increasing expansion of Web technology, a large amount of data is available to researchers. Extracting information from these data is one of the requirements for many organizations and business centers. In recent years, the massive amount of Twitter's social networking data has become a platform for ...

Subject Specific Stream Classification Preprocessing Algorithm for Twitter Data Stream

Micro-blogging service Twitter is a lucrative source for data mining applications on global sentiment. But due to the omnifariousness of the subjects mentioned in each data item; it is inefficient to run a data mining algorithm on the raw data. This paper discusses an algorithm to accurately classify the entire stream in to a given number of mutually exclusive collectively exhaustive streams up...

Loose Tweets: An Analysis of Privacy Leaks on Twitter Loose Tweets: An Analysis of Privacy Leaks on Twitter

Preprocessing Egyptian Dialect Tweets for Sentiment Mining

Research done on Arabic sentiment analysis is considered very limited almost in its early steps compared to other languages like English whether at document-level or sentence-level. In this paper, we test the effect of preprocessing (normalization, stemming, and stop words removal) on the performance of an Arabic sentiment analysis system using Arabic tweets from twitter. The sentiment (positiv...