5468 Turkish Journal of Computer and Mathematics Education Vol.12 No.3(2021), 5468-5474 An Application of Emotion Detection in Sentiment Analysis on Movie Reviews Ria Ambrocio Sagum, MCS a,b a,b College of Computer and Information Sciences,Research Management Office,Polytechnic University of the Philippines, Sta. Mesa, Manila Article History: Received: 10 November 2020; Revised 12 January 2021 Accepted: 27 January 2021; Published online: 5 April 2021 _____________________________________________________________________________________________________ Abstract: The research focus on the issue of accuracy for sentiment analysis. The researcher experimented on emotion detection result to be used in sentiment analysis. The emotions that were included in this research are happiness, sadness, anger, and fear. Once emotion was detected the system will then use it to know the sentiment of the person on a particular movie. This paper aims to measure the accuracy in sentiment analysis enhanced by emotion detection and to know whether emotion detection plays a key role in reading sentiment analysis. Keywords: Emotion Detection, Language Processing, Sentiment Analysis on Movie Reviews, Emotion Detection on Movie Reviews ___________________________________________________________________________ 1. Introduction To date the use of sentiment analysis in social media monitoring became the latest trend, because of this people gain an overview of the public opinion in a particular topic. The ability to extract insights from social data is common and is being adopted by organizations. In the movie industry, some of the moviegoers check first the review of a particular movie before watching it in the cinema. A good review can sometimes be considered as a good box-office success. But one cannot rely on a person review alone, one needs to consider that different person has different insight based on their lifestyle and cultural belief. It is said that a movie can create change cultural climate of the viewer. A study of Persson [1], exemplifies that a cinema movie may introduce a new theme, lifestyle, fashion style, and or different conventions that will change the way the critics, authors, and audience understand its literature. As how cinema affect persons lifestyle and beliefs that give definition to human society, its semiotics and articulation differs. With this we can say that a movie can be seen by people differently based on their culture and lifestyle. Given this still sentiments of people doing the review is important and must be accurate to save time and money for some people. This time people do not want to expect something that cannot be given by the movie. A good sentiment analysis can help these people by simply looking on the sentiments result. For some reviews you can see it thru the stars, but sometimes these are just numerical. Sentiment analysis as application of Natural Language Processing (NLP) does not compute numerical feedback to give the sentiments rather use text analysis of the sentences based on the reviewers post or feedbacks. There are existing sentiment analysis that are being used for reviews but researchers are still trying to find different ways to be able to accurately analyze a sentiment for a review. Different variables are being used to know if the variable can help increase the accuracy of a sentiment analysis system. As finding for a variable that may help to increase the accuracy of a sentiment analyzer. The researcher upon reading literatures in emotion detection will apply the use of detected emotion in a sentence and use it to define the sentiment of a sentence. A system will be developed as a sentiment analyzer with the inclusion of emotion detection as a basis for sentiment analysis computation. The analysis will start once move review were fed into the machine. Sentiment analysis will then begin. The parameters of this system were trained to maximize prediction accuracy given the target labels in the training set. The researcher would like to find out how will sentiment analysis enhanced by emotion detection will perform against the same sentiment analyzer machine without applying the emotion detection. once the machine is trained? There will only be four core emotion the machine can detect in this system. The movie review as input to the machine is in Filipino(Tagalog) language. The system develop will only have four output as its emotions [1] and these are: Research Article Research Article Research Article Research Article Research Article Research Article