1 CHAPTER ONE INTRODUCTION 1.1 Background to the study The study of sentiment analysis on movie reviews has its roots in the field of natural language processing (NLP). NLP is an interdisciplinary field that focuses on the development of computational methods for processing human language. The goal of sentiment analysis is to automatically determine the sentiment expressed in text data, such as product reviews, social media posts, or movie reviews. Sentiment analysis has many applications, including marketing research, customer service, and public opinion analysis. The study of sentiment analysis on movie reviews is particularly important for the film industry, as movie reviews play a crucial role in influencing the public's perception of a movie. Positive reviews can increase the box office revenue of a movie, while negative reviews can lead to poor box office performance. Over the years, researchers have developed various approaches to sentiment analysis on movie reviews. Early approaches relied on lexicon-based methods, which use pre-defined lists of positive and negative words to determine the sentiment expressed in a text. More recently, machine learning algorithms have been developed to automatically learn the features and patterns that are indicative of positive or negative sentiment in movie reviews. The study of sentiment analysis on movie reviews is an active area of research, with ongoing efforts to improve the accuracy and robustness of sentiment analysis algorithms. Researchers are also exploring new applications of sentiment analysis in the film industry, such as predicting the success of a movie based on pre-release sentiment analysis of social media discussions. The study of sentiment analysis can be traced back to the early days of natural language processing, which is a field of computer science that focuses on the interaction between computers and human language. In the 1950s and 1960s, researchers began to explore the use of computers to analyze and understand human language, and early work in this area focused on simple tasks such as text classification and information retrieval. Over the years, as computing power and machine learning techniques have advanced, sentiment analysis has become more sophisticated, and researchers have developed more accurate methods for analyzing the sentiment expressed in text. One of the key challenges in this field is the ambiguity of human language, as words and phrases can have multiple meanings and can be used in different ways to express different sentiments. In the context of movie reviews, sentiment analysis can provide valuable insights into people's opinions and attitudes towards different movies, which can be used to inform marketing strategies and improve the overall movie-watching experience. For example, movie studios can use sentiment analysis to gauge