Design, Improvement, Development, and Performance Analysis of a Collection of Model Developed From Naïve Bayes and Maximum Entropy Opinion Mining Classifiers for Movie Reviews
About this article
DOI:
https://doi.org/10.14419/ijet.v7i2.33.17908Keywords:
Reviews, measure, Sentiment analysis, Naïve Bayes, Maximum Entropy, bigrams, ensemble model, hybrid algorithmAbstract
The internet is a basic platform for people from every one walks of life to interconnect and convey opinions on the topic of their choice. Almost every website asks for comments, suggestions and reviews. Exploring opinion and determining a person’s views is itself a large subject in computer science, known as Opinion Mining, also called Sentiment Analysis There are different sentiment classifiers, the most admired of which are the Naïve Bayes classifier, maintain Vector Machines (SVM), Maximum Entropy classifier, to name a few. In this paper, here we are analyzing the efficient performance of the Naïve Bayes also about the Maximum Entropy classifiers. Here we analyze and examine how bigrams perform better than unigrams in sentiment analysis. We further propose a serialized ensemble model of the two as a hybrid algorithm and analyze its performance as well.
References
Wikipedia.org ‘Intenet Movie Database’, 2015 [Onlne]. Available:https://en.wikipedia.org/wiki/Internet_Movie_Database
Jayashri Khairnar, Mayura Kinikar, ‘Machine Learning Algorithms for Opinion Mining and Sentiment Classification’, International Journal of Scientific and Research Publications, Volume 3, Issue 6, June 2013
Nltk.org ‘NLTK 3.0 Documentation’, [Online]. Available: http://www.nltk.org/
Wikipedia.org ‘Precision and recall’, 2015 [Online]. Available: https://en.wikipedia.org/wiki/Precision_and_recall
Wikipedia.org ‘Bag-of-words model’, 2015 [Online]. Available: https://en.wikipedia.org/wiki/Bag-of-words_model
View more references (16)
Wikipedia.org ‘Naive Bayes Classifier’, 2015, [Online]. Available: https://en.wikipedia.org/wiki/Naive_Bayes_classifier
Nlp.stanford.edu ‘Naïve Bayes text classification’, 2008, [Online] Available: http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html
Wikipedia.org ‘Entropy (information theory)’, 2015 [Online]. Available: https://en.wikipedia.org/wiki/Entropy_(information_theory)
Wikipedia.org ‘Principle of maximum entropy’, 2015 [Online]. Available: https://en.wikipedia.org/wiki/Principle_of_maximum_entropy
Bayes.wustl.edu ‘Information Theory and Statistical Mechanics’ 1957, [Online] Available: http://bayes.wustl.edu/etj/articles/theory.1.pdf
Sentiment.christopherpotts.net, ‘Sentiment Symposium Tutorial: Classifiers’ 2011, [Online], Available: http://sentiment.christopherpotts.net/classifiers.html#maxent
Wikipedia.org ‘Bigram’, 2015, [Online] Available: https://en.wikipedia.org/wiki/Bigram
Streamhacker.com ‘TEXT CLASSIFICATION FOR SENTIMENT ANALYSIS – ELIMINATE LOW INFORMATION FEATURES, 2010, [Online]. Available: http://streamhacker.com/tag/feature-extraction/
Wikipedia.org ‘Ensemble learning’, 2015, [Online], Available: https://en.wikipedia.org/wiki/Ensemble_learning
AnalyticsVidhya.com, ‘Basics of Ensemble Learning Explained in
Simple English’, 2015,[Online],Available: http://www.analyticsvidhya.com/blog/2015/08/introduction-ensemble-learning/
‘The Optimality of Naive Bayes’, Harry Zhang, [Online], Available: http://www.aaai.org/Papers/FLAIRS/2004/Flairs04-097.pdf
Wikipedia.org ‘Confusion Matrix’, 2015, [Online], Available: https://en.wikipedia.org/wiki/Confusion_matrix
Data School ‘Simple guide to confusion matrix terminology’, 2014,[Online], Available http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
Christine Day, ‘The Importance of Sentiment Analysis in Social Media
Analysis’, [Online], Available: