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Research on Sentiment Classification of Chinese Micro Blog Based on Machine Learning
| Content Provider | Semantic Scholar |
|---|---|
| Author | Liu, Dongqing |
| Copyright Year | 2013 |
| Abstract | This thesis has conducted an empirical research on sentiment classification of micro blog in three machine learning algorithms, three feature selection algorithms and three feature item weighting algorithms. As the experimental result shows, considering different feature weighting algorithms, SVM and Naïve Bayes have their own advantages, and Information Gain (IG) feature selection algorithm is apparently more effective than other methods. Considering the three factors as a whole, it is most effective to have sentiment classification on micro blog by adopting SVM, IG and TF-IDF (Term Frequency-Inverse Document Frequency) as feature items weighting. It has compared the generality of classification model between micro blog comments and ordinary comments in the field of films, and as a result, the experimental results show that the performance of sentiment classification relies on the style of reviews. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.aicit.org/JDCTA/ppl/JDCTA2521PPL.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Blog Feature selection Kullback–Leibler divergence Machine learning Naive Bayes classifier Review [Publication Type] Selection algorithm Statistical classification Tf–idf |
| Content Type | Text |
| Resource Type | Article |
