Effect of Supervised and Unsupervised Algorithm for Cross Domain Sentiment Analysis
Vaishali Arya1, Rashmi Agrawal2

1Vaishali Arya*, Manav Rachna International Institute of Research & Studies, Faridabad, India.
2Rashmi Agrawal, Manav Rachna International Institute of Research & Studies, Faridabad, India.
Manuscript received on May 16, 2020. | Revised Manuscript received on June 01, 2020. | Manuscript published on June 10, 2020. | PP: 719-723 | Volume-9 Issue-8, June 2020. | Retrieval Number: H6725069820/2020©BEIESP | DOI: 10.35940/ijitee.H6725.069820
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Today we are living in the “information age” where data is the capital of the new economy. With the rapidly growing data every day on online portals and social networking websites, today industries are collecting and analyzing more data than before. Though data is readily available but finding valuable insights out of it is a real task. With easy accessibility of the data, new technologies, and a cultural shift towards data-driven decision making drives the need for Sentiment Analysis (SA) and makes it relevant in most of the domains like politics, marketing, healthcare, etc. This rapidly increasing information on different domains has motivated researchers to develop a cross-domain sentiment analysis model. For the development of this model, we have analyzed the performance of supervised and unsupervised models on benchmark datasets for the cross-domain analysis. The models chosen for the supervised is the Support Vector Machine (SVM) and for the unsupervised approach we have used a combination of Vader wherein the testing results showed that the supervised algorithms performed well in comparison to the unsupervised algorithm. 
Keywords: Cross-Domain Sentiment Analysis, Supervised Algorithms for Cross-Domain, SVM for SA, Unsupervised Algorithms for Cross-Domain, Vader SA.
Scope of the Article: Parallel and Distributed Algorithms