Accurate Sentiment Analysis using Enhanced Machine Learning Models
Journal: International Journal of Science and Research (IJSR) (Vol.4, No. 9)Publication Date: 2015-09-05
Authors : Rincy Jose; Varghese S Chooralil;
Page : 252-254
Keywords : Sentiment Classification; Negation Handling; sentiment Analysis; Feature Selection;
Abstract
Sentiment analysis is the computational study of opinions, sentiments, subjectivity, evaluations, attitudes, views and emotions expressed in text. Sentiment analysis is mainly used to classify the reviews as positive or negative or neutral with respect to a query term. This is useful for consumers who want to analyse the sentiment of products before purchase, or viewers who want to know the public sentiment about a new released movie. Here i present the results of machine learning algorithms for classifying the sentiment of movie reviews which uses a chi-squared feature selection mechanism for training. I show that machine learning algorithms such as Naive Bayes and Maximum Entropy can achieve competitive accuracy when trained using features and the publicly available dataset. It analyse accuracy, precision and recall of machine learning classification mechanisms with chi-squared feature selection technique and plot the relationship between number of features and accuracy using Naive Bayes and Maximum Entropy models. Our method also uses a negation handling as a pre-processing step in order to achieve high accuracy.
Other Latest Articles
- Review Technique for Exploration of the Manufacturing Line for Improved Production
- Temperature Control for Different Industrial Heat Exchanger by Using PID
- Characteristics of Linkages between Kano State Agricultural and Rural Development Authority (KNARDA) and Technology Business Incubation Centre (TBIC) in Agricultural Mechanization in Kano State, Nigeria
- Electrocoagulation of Waste Water and Treatment of Rice Mill Waste Water: A Review
- Trends of Antibiotic Use Among the Indoor Patients of Medicine and Pediatric Ward at A Tertiary Care Hospital
Last modified: 2021-06-30 21:53:24