An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis
Table 5: Evaluation
['Datasets', 'ACACCE', 'SVM', 'RF', 'J48', 'NB', 'MNB', 'Clustering (Li and Liu,  2012 )', 'Baseline']
Four of them are supervised classifiers namely Support Vector Machine (SVM), Random Forest (RF), decision tree (J48), Naive Base (NB) and Multinominal Naive Base (MNB). For conducting experiments using supervised classifiers, all part of speach tags are used as a set of features and TF-IDF weight is also utilized. We also report results of a clustering based method by Li and Liu In addition to that, a baseline classifier is constructed to classify a document by aggregating SWN’s average scores of its adjectives and adverbs to determine the polarity. The results show that the average rate of the baseline classifier’s accuracy is comparably low which is probably because the average score extracted from SWN does not reflect an accurate sentiment strength of a term which is mainly because the language context in which this term appears is neglected. The average of the accuracy rate of ACACCE is very close to the average rate of SVM’s accuracy which is the best average rate among the compared methods. ACACCE also yields the best performances on three datasets and comparable performances on the other four datasets. One of the research observations is that the generalization performance of ACACCE is enhanced which is a result of applying contextual analysis and using various data representation. For example, ACACCE yields the higher accuracy when operating on Kitchen dataset whereas the accuracy rates of the other algorithms are comparably low when processing this dataset. This is because of the two processing phases of ACACCE where the processed text will be automatically analyzed in the contextual analysis phase, then in the second phase different data representations will be combined in the ensemble method.