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Performance Analysis of Machine Learning and Deep Learning Algorithms for Sentiment Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 44 |
| Year of Publication: 2021 |
| Authors: Mugdha Deokar, Varun Godse |
10.5120/ijca2021921854
|
Mugdha Deokar, Varun Godse . Performance Analysis of Machine Learning and Deep Learning Algorithms for Sentiment Analysis. International Journal of Computer Applications. 183, 44 ( Dec 2021), 30-34. DOI=10.5120/ijca2021921854
Abstract
Public opinion or review of a product, a movie, or a restaurant is a key driver of trends and influences how a person chooses a particular service. This out-pour of opinion contributes to the overall information and becomes a crucial tool to analyze the sentiment towards the service provider. A large pool of information thus can be processed with the help of Natural Language Processing (NLP) tools to find out how good or bad a movie, product, or restaurant is. A dataset containing sentences from websites like Amazon, IMDb, and Yelp to study and predict people's sentiment towards a particular service is used. To achieve the results, Natural Language Processing for sentiment analysis is utilized. Initial step of the project includes designing a model based on six machine learning algorithms like Support Vector Machine, Random Forest, and other algorithms for classification purposes. Next step was using a voting classifier to extract the best features of each algorithm and get a conclusive result. Further, Recurrent Neural Networks and Long Short Term Memory(LSTMs) leverage the power of deep learning to achieve higher accuracy and better results. Also, the BERT model is used to perform sentiment analysis. Thus, the paper aims to compare various possible algorithms that can be used for sentiment analysis using machine learning, deep learning, and natural language processing. In the end, it is investigated if the public response to the service provided is either positive or negative.
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