Content Mining of Microblogs

Cingiz, Mustafa Özgür; Di̇ri̇, Banu

doi:10.1109/asonam.2012.151

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Term Frequency -Inverse Document Frequency approach is used to weight feature set [21]. This method uses two different aspects while weighting a term, one is Term Frequency which means the frequency of the term in every document with respect to each category.…”

Section: Term Weightingmentioning

confidence: 99%

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Investigating Sentimental Relation between Social Media Presence and Academic Success of Turkish Universities

Gunduz

Demirhan

Sağıroğlu

2014

2014 13th International Conference on Machine Learning and Applications

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In this study an approach that uses social networking data for developing sentiment analysis system is proposed. With the help of developed software, it is tried to find out whether there is any relation between universities' academic success and sentiment of the public about them in social media. After collecting enough text based data from Twitter, preprocessing of data is carried out and final data is trained by means of Naïve Bayes Classifier. After testing process, experimental results have shown that developed sentiment analysis system can classify the tweets about top 10 universities according to URAP rankings in terms of their sentiment with the 72.33% success rate, and proposed methodology can be used by universities for understanding sentiment of the public about them in social media.

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Section: Term Weightingmentioning

confidence: 99%

“…TF-IDF value can of a term simply calculated by multiplying that terms TF value and IDF value for each and every one of the classes. After each term has a TF-IDF weight value for each sentiment class this step is finished [21].…”

Section: Term Weightingmentioning

confidence: 99%