Sentiment Analysis using Artificial Neural Network
Niharika1, Sona Malhotra2
1Niharika*, Computer Science and Engineering, University Institute of Engineering and Technology, Kurukshetra University (Kurukshetra), Kurukshetra, India.
2Sona Malhotra, Computer Science and Engineering, University Institute of Engineering and Technology, Kurukshetra University University (Kurukshetra), Kurukshetra, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3267-3273 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6450018520/2020©BEIESP | DOI: 10.35940/ijrte.E6450.018520
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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: In the modern era, there are massive amount of web resources present such as blogs, review sites and discussion forums. These resources form the platform where users can share their opinions or reviews` about anything whether it is a product, movie or a restaurant. Analysis of public sentiments deals with the determination of the polarity of different public opinions or reviews into either the category of positive, negative or neutral. Thus, there comes the need of sentiment analysis which not only helps other individual to make a decision regarding buying a product, visiting a restaurant or watching a movie but also helps the producers of various products and owners of different restaurants to gain the knowledge of preferences of customers, so that it could be possible to increase the profit and economic value. The paper presents a survey with main focus on performance of different artificial neural networks used for opinion mining or sentiment analysis while it also includes various machine learning approaches such as Naïve Bayes, Support Vector Machine, lexicon-based approach and Maximum Entropy.
Keywords: Sentiment Analysis, Supervised Learning, Machine Learning, Opinions, Artificial Neural Network, Linear Classifier, Naïve Bayes, Support Vector Machine.
Scope of the Article: Machine Learning.