In this section, we describe the experimental setup of our several runs.
Baseline Run: At rst, we used the training dataset of 1.6 million tweets of sentiment140 dataset to train the Naive-Bayes classi er for classifying the sentiment of the test set, which we considered as our baseline. In this run, we just use the bag-of-words (BoW) feature and did not perform any text preprocessing task. Run1: At Run1, we consider the similar kind of setup like Baseline Run. But here we incorporate our text preprocessing strategy to improve the classi cation result. Run2: At Run2, we trained the Naive-Bayes classi er with several sentiment lexicons instead of using large training dataset of 1:6 million tweets. We also incorporate our text preprocessing …show more content…
The strategy is that at rst our rule-based classi er is applied to classify the tweets sentiment as positive, negative or unknown. As, our goal is to classify the tweets sentiment only positive or negative class. So, for the tweets that are labeled as unknown by the rule-based classi er, we consider the predictions of Naive-Bayes classi er as the nal labels.
Run4: At this run, we combined our proposed rule-based classi er with the setup of Run2, to improve the classi cation performance. Here, we use the similar kind of combination strategy already described in the experimental setup of Run3.
Run5: At this run, we used the training dataset of 1:6 million tweets of sentiment140 dataset to train the Multiclass SVM classi er from cornell university [67] for classifying the sentiment of the test set. In this run, we use our preprocessing strategy and the bag-of-words (BoW) feature. For feature weighting, we use the
TF-IDF weighting …show more content…
Run8: At Run8, we trained the weka's [69] multinomial Naive-Bayes classi- er with our selected 35 features. Then, we combined our proposed rule-based classi er to improve the classi cation performance. Here, we also use the similar kind of combination strategy already described in the experimental setup of
Run3.
Run9: At Run9, at rst our rule-based classi er is applied to classify the tweet sentiment as positive, negative or unknown. For the tweets that are labeled as unknown by the rule-based classi er, we consider the majority voting count based predictions from several classi ers as stated below:
Probabilistic Naive Bayes Classi er: Trained with the sentiment140 dataset.
(BoW feature)
Probabilistic Naive Bayes Classi er: Trained with our combined sentiment lexicons. (BoW feature)
Probabilistic Multiclass SVM Classi er: Trained with the sentiment140 dataset. (BoW feature with TF-IDF weighting scheme)
Probabilistic SMO Classi er: Trained with the sentiment140 dataset. (Selected
35 features)
Probabilistic Naive Bayes Multinomial Classi er: Trained with the sentiment140 dataset. (Selected 35
Baseline Run: At rst, we used the training dataset of 1.6 million tweets of sentiment140 dataset to train the Naive-Bayes classi er for classifying the sentiment of the test set, which we considered as our baseline. In this run, we just use the bag-of-words (BoW) feature and did not perform any text preprocessing task. Run1: At Run1, we consider the similar kind of setup like Baseline Run. But here we incorporate our text preprocessing strategy to improve the classi cation result. Run2: At Run2, we trained the Naive-Bayes classi er with several sentiment lexicons instead of using large training dataset of 1:6 million tweets. We also incorporate our text preprocessing …show more content…
The strategy is that at rst our rule-based classi er is applied to classify the tweets sentiment as positive, negative or unknown. As, our goal is to classify the tweets sentiment only positive or negative class. So, for the tweets that are labeled as unknown by the rule-based classi er, we consider the predictions of Naive-Bayes classi er as the nal labels.
Run4: At this run, we combined our proposed rule-based classi er with the setup of Run2, to improve the classi cation performance. Here, we use the similar kind of combination strategy already described in the experimental setup of Run3.
Run5: At this run, we used the training dataset of 1:6 million tweets of sentiment140 dataset to train the Multiclass SVM classi er from cornell university [67] for classifying the sentiment of the test set. In this run, we use our preprocessing strategy and the bag-of-words (BoW) feature. For feature weighting, we use the
TF-IDF weighting …show more content…
Run8: At Run8, we trained the weka's [69] multinomial Naive-Bayes classi- er with our selected 35 features. Then, we combined our proposed rule-based classi er to improve the classi cation performance. Here, we also use the similar kind of combination strategy already described in the experimental setup of
Run3.
Run9: At Run9, at rst our rule-based classi er is applied to classify the tweet sentiment as positive, negative or unknown. For the tweets that are labeled as unknown by the rule-based classi er, we consider the majority voting count based predictions from several classi ers as stated below:
Probabilistic Naive Bayes Classi er: Trained with the sentiment140 dataset.
(BoW feature)
Probabilistic Naive Bayes Classi er: Trained with our combined sentiment lexicons. (BoW feature)
Probabilistic Multiclass SVM Classi er: Trained with the sentiment140 dataset. (BoW feature with TF-IDF weighting scheme)
Probabilistic SMO Classi er: Trained with the sentiment140 dataset. (Selected
35 features)
Probabilistic Naive Bayes Multinomial Classi er: Trained with the sentiment140 dataset. (Selected 35