ACCURACY IN BINARY, TERNARY AND MULTI-CLASS CLASSIFICATION SENTIMENTAL ANALYSIS-A SURVEY
Main Article Content
Abstract
Sentiment analysis is nowadays quite a hot topic for research. Since most of the research is been done on the data acquired from the social networking sites mostly twitter and is subsequently classified into binary classification (“positive†and “negativeâ€) or the ternary classification (“positiveâ€, “negativeâ€, and “neutralâ€). The binary and ternary classification is not going to serve the sole purpose of sentimental analysis. Multi-class classification can help in getting the essence and core message from the data. Whether it is binary, ternary or multi-class classification, the main objective always remains the accuracy of finding the actual sentiments. Since ample work has been done on binary and ternary classification and the better accuracy has been achieved but in case of multi-class classification accuracy is still a challenge. In this paper, we will analyze different machine learning algorithms and techniques that have been used in the sentimental analysis and the accuracy achieved using those algorithms and techniques.
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.