Analysis of Internet Movie Database with Global Vectors for Word Representation

Abstract

Sentiment analysis (SA) involves utilizing natural language processing (NLP) methods to identify the sentiment conveyed by a given text. This study is grounded on the dataset sourced from the internet movie database (IMDB), encompassing evaluations of films and their corresponding positive or negative classifications. Our research experiment aims to ascertain the model with the highest accuracy and generality. Our research utilizes diverse classifiers, comprising unsupervised learning approaches such as Valence Aware Dictionary and sEntiment Reasoner (VADER) and Text Blob, alongside Supervised Learning methods like Naïve Bayes, which encompasses both the Bernoulli NB and Multinomial NB. Several methodologies have been utilized, including the Count Vectorizer, and the Term Frequency-Inverse Document Frequency model (TFIDF) Vectorizer. Subsequently, word embedding and bidirectional LSTM are executed, utilizing various embeddings such as the Long Short-Term Memory (LSTM) base model. Finally, GloVe embeddings achieve the best performance with an accuracy of 90.64% and a sensitivity of 91.07%.