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Multiclass Classification of Tweets and Twitter Users Based on Kindness Analysis
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhou, Wanzi Huang, Xinyuan |
| Copyright Year | 2016 |
| Abstract | In 2015, Cheng et al [1] from Stanford and Cornell Universities have developed a logistic regression model, using labeled posts to predict antisocial behavior in online discussion communities. Their study focuses on spotting out whether a user is a troll or not, which is a binary classification problem. Earlier in 2011, Sood et al [2] from Pomona College and Yahoo Company developed a model for automatic identification of personal insults on social news sites, which is also a supervised learning work and belongs to binary classification problem. They got their data labeled via Amazon Mechanical Turk. Meanwhile, sentiment analysis using Twitter data has been a popular topic in machine learning. Bifet and Frank [3] conducted a supervised learning with multinomial naive Bayes classifier to predict the sentiment and opinion of tweets. Pak and Paroubek [4] improved this model by better cleaning the input data. Agarwal et al [5] from Columbia University further explored tweets with a 3-way classification, namely positive, negative and neutral. All the mentioned research studies are supervised learning, however, it is infeasible to label enough training data in short time. Thus, different from former work, we propose to give each tweet/Twitter user a kindness rating, leading to an unsupervised multinomial classification or regression. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://cs229.stanford.edu/proj2016/report/HanHuangZhou-MulticlassClassificationOfTweetsBasedOnKindnessAnalysis-report.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
