SENTIMENT ANALYSIS OF HYBRID CARS REVIEWS USING MACHINE LEARNING
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Date
2021
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Publisher
UMT, Lahore
Abstract
The increase in usage of Oil and Gas in has led to environmental problems such as global warming, climate change and shortage of crude oil. Due to these reasons people around the world have started to use Hybrid Technology automobiles in the daily life. With people using hybrid automobiles in daily life, there is need of a technique which can help provide information about cars that are in accordance with the user's wishes, namely the recommendation system. This study proposes a sentiment analysis-based model to mine the consumer’s attitude and emotion which they expressed in the form of reviews on various automobile websites. There are two main objectives of this study: 1) develop an annotated benchmark corpus manually for the English Language reviews for the sentiment analysis, 2) to assess sentiment analysis methods and techniques using the Ngram, and Support Vector Machine (SVM) and Long Short Term Memory(LSTM). Three experts annotated the corpus in two categorize: positive and negative with Cohen's Kappa score of 0.90. Lastly the proposed model was analyzed by comparing the results of both models SVM and Naive bayes model.