Twitter sentiment classification using Naive Bayes based on trainer perception

Sentiment classification gains a lot of attention nowadays. For a university, the knowledge obtained from classifying sentiments of student learning in courses is highly valuable, and can be used to help teachers improve their teaching skills. In this research, sentiment classification based on text embedding is applied to enhance the performance of sentiment classification for Thai teaching evaluation. Text embedding techniques considers both syntactic and semantic elements of sentences that can be used to improve the performance of the classification. This research uses two approaches to apply text embedding for classification. The first approach uses fastText classification. According to the results, fastText provides the best overall performance; its highest F-measure was at 0.8212. The second approach constructs text vectors for classification using traditional classifiers. This approach provides better performance over TF-IDF for k-nearest neighbors and naïve Bayes. For naïve Bayes, the second approach yields the best performance of geometric mean at 0.8961. The performance of TF-IDF is better suited to using decision tree than the second approach. The benefit of this research is that it presents the workflow of using text embedding for Thai teaching evaluation to improve the performance of sentiment classification. By using embedding techniques, similarity and analogy tasks of texts are established along with the classification.

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Sentiment classification on big data using Naïve bayes and logistic regression

2017 International Conference on Computer Communication and Informatics (ICCCI) ◽

10.1109/iccci.2017.8117734 ◽

2017 ◽

Cited By ~ 12

Author(s):

Anjuman Prabhat ◽

Vikas Khullar

Keyword(s):

Logistic Regression ◽

Big Data ◽

Naive Bayes ◽

Naïve Bayes ◽

Sentiment Classification

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Analysis of Sentiment Classification of Hotel Reviews Based on Multinomial Naive Bayes

2020 The 11th International Conference on E-business, Management and Economics ◽

10.1145/3414752.3414796 ◽

2020 ◽

Author(s):

Yang Zhirui ◽

Li Chunyan

Keyword(s):

Naive Bayes ◽

Naïve Bayes ◽

Sentiment Classification

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Less Sparse Feature Set with Meta Heuristic Weighted Classifier for Tweet Sentiment Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b6699.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1328-1334

Keyword(s):

Naive Bayes ◽

Hybrid Approach ◽

Naïve Bayes ◽

Sentiment Classification ◽

Bayes Classifier ◽

Hybrid Approaches ◽

Machine Leaning ◽

Feature Weight ◽

Precision And Accuracy ◽

Better Than

Twitter using Machine Leaning Techniques has been done. While consideration Bigram, Unigram,. SVM and naïve Bayes classifier which hybrid with PSO and ACO for effective feature weight. In Fig. 4.9 compare all experiment by on graph which shows that SVM_ACO and SVM_PSO better perform than SVM. NB_ACO and NB_PSO perform better than NB but if compare between hybrid approaches then SVM_PSO show 81.80% accuracy,85% precision and 80% recall. IN case of naïve Bayes NB_PSO 76.93% accuracy,76.24 precision and 82.55% recall, so experiments conclude that Naive Bayes improve recall and SVM improve precision and accuracy when use as hybrid approach.

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