SENTIMENT POLARITY WITH SENTIWORDNET AND MACHINE LEARNING CLASSIFIERS
Main Article Content
Abstract
: Sentiment classification is concerned with using automated methods for predicting the orientation of subjective content on textual content documents, with applications on some of areas consisting of recommended and advertising and marketing systems, customer intelligence and information retrieval. SentiWordNet is not anything however an opinion lexicon derived from the WordNet database in which each term is related to numerical scores indicating their sentiments. This research offers the results of making use of the SentiWordNet lexical resource to the hassle of computerized sentiment classification on labelled dataset. Our method incorporates counting positive and negative scores to decide sentiment orientation, and an improvement is provided by means of constructing a information set of applicable features using SentiWordNet as supply, and additionally implemented to a machine learning classifier. We compared the accuracies results obtained with SentiWordNet and Machine Learning Classifiers.
Downloads
Download data is not yet available.
Article Details
Section
Articles
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.