Sentimental Analysis on Movie Review using NLP

Sentimental Analysis on Movie Review using NLP

Saurabh Chachere¹, Aditya Kshirsagar², Saurav Yadav³, Sanchet Manohare⁴, Sagar Dongre⁵ , Aditya Chauhan⁶

¹Saurabh Chachere, Computer Science & Engineering &Nagpur Institute of Technology, Nagpur

²Aditya Kshirsagar, Computer Science & Engineering &Nagpur Institute of Technology, Nagpur

³Saurav Yadav, Computer Science & Engineering &Nagpur Institute of Technology, Nagpur

⁴Sanchet Manohare, Computer Science & Engineering &Nagpur Institute of Technology, Nagpur

⁵Aditya Chauhan, Computer Science & Engineering &Nagpur Institute of Technology, Nagpur

⁶Sagar Dongre, Computer Science & Engineering &Nagpur Institute of Technology, Nagpur

Abstract

Nowadays, e-learning-based teaching methodologies and online classes are gaining popularity, providing a virtual platform for online education from anywhere in the world. Social networks are widely distributed, generating different opinions on various perspectives of life through web messages. This textural information is highly valuable in performing sentiment analysis and opinion mining expressed through the text, providing students' feelings with statements showing agreement or disagreement in the comment sections to reveal their negative or positive sentiments towards learning. The primary aim of this paper is to design a new sentiment analysis model for e-learning platforms using natural language processing techniques. The researchers initially gathered standard text data on e-learning platforms with user reviews from benchmark resources, which were then subjected to pre-processing techniques to avoid unnecessary content for maximizing sentiment analysis performance. Word-to-vector conversion using the glove embedding scheme was carried out to obtain relevant data for sentiment analysis, followed by sentiment classification through Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU). Finally, hybrid deep learning was used to analyze sentiments in the field of e-learning, revealing promising results in sentiment analysis tasks.

Key Words: Sentiment Analysis, E-Learning Platform, Natural Language Processing, Convolutional Neural Networks, Gated Recurrent Unit.