NDLI: Sentiment Analysis of Movie Reviews using ELMO Word Representation on Hybrid Neural Network

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Sentiment Analysis of Movie Reviews using ELMO Word Representation on Hybrid Neural Network

Content Provider	TKM College of Engineering
Advisor	John, Ansamma
Author	Musthafa, Shahana
Abstract	Sentiment analysis is an active research area in natural language processing where underlying emotions expressed in textual data are analysed and identified. It can either be categorized as positive and negative or multiclass such as happiness, sadness, anger, fear, etc that enables to understand the social sentiment of a particular brand, product or service while monitoring online conversations. In addition, it can be used for studying psychological problems, conducting emotional marketing and for improving customer experience. The overload of data in today’s environment makes it impossible to analyze it manually and makes systematic sentiment analysis even more relevant. Many approaches were used earlier for sentiment analysis like ruled - based analysis and machine learning based analysis. Recent research indicates that deep learning techniques shows better performance for sentiment analysis. Hence the hybrid neural network model consisting of CNN and BiL STM is introduced. Initially, text are converted into vector format using embedding languages such as Word2Vec, GloVe and ELMo language model. Then CNN allows extracting local features of text vectors while global features are extracted by BiLSTM. The features extracted by the two models are then used together for performing the sentiment analysis. Experiments are conducted on IMDB movie reviews dataset. The trained hybrid neural network can then automatically classify the sentences achieving an accuracy rate of 91% for text categorization compared to other state-of-art approaches.
Language	English
Publisher Date	2022-09-01
Access Restriction	Open
Subject Keyword	Word2Vec Language Model Hybrid Neural Network Model GloVe Language Model Deep Learning ELMo Language Model
Content Type	Text
Resource Type	Thesis

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