Movie Reviews Classification through Facial Image Recognition and Emotion Detection Using Machine Learning Methods

Abstract

The critical component of HCI is face recognition technology. Emotional computing heavily relies on the identification of facial emotions. Applications for emotion-driven face animation and dynamic assessment are numerous (FER). Universities have started to support real-world face expression recognition research as a result. Short video clips are continually uploaded and shared online, building up a library of videos on various topics. The enormous amount of movie data appeals to system engineers and researchers of autonomous emotion mining and sentiment analysis. The main idea is that categorizing things may be done by looking at how individuals feel about specific issues. People might choose to have a basic or complex facial appearance. People worldwide continually express their feelings through their faces, whether they are happy, sad, or uncertain. An online user can visually express themselves through a video’s editing, music, and subtitles. Additionally, before the video data can be used, noise in the data must frequently be eliminated. Automatically figuring out how someone feels in a video is a challenging task that will only get harder over time. Therefore, this paper aims to show how facial recognition video analysis can be used to show how sentiment analysis can help with business growth and essential decision-making. To determine how people are affected by reviewers’ writing, we use a technique for deciding emotions in this analysis. The feelings in movies are assessed using machine learning algorithms to categorize them. A lightweight machine learning algorithm is proposed to help in Aspect-oriented emotion classification for movie reviews. Moreover, to analyze real and published datasets, experimental results are compared with different Machine Learning algorithms, i.e., Naive Bayes, Support Vector Machine, Random Forest, and CNN. The proposed approach obtained 84.72 accuracy and 79.24 sensitivity. Furthermore, the method has a specificity of 90.64 and a precision of 90.2. Thus, the proposed method significantly increases the accuracy and sensitivity of the emotion detection system from facial feature recognition. Our proposed algorithm has shown contribution to detect datasets of different emotions with symmetric characteristics and symmetrically-designed facial image recognition tasks.

Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning

“…[ 36 ] demonstrated that it could classify between baseline and high arousal and valence levels with an accuracy of 80%. Some studies have aimed to determine the specific emotion displayed by participants, achieving accuracies of 65.75% [ 9 ] and 84.72% [ 24 ]. However, these studies do not allow us to determine whether participants are in a state of stress or relaxation.…”

“…The use of Artificial Intelligence (AI) techniques such as ML and Deep Learning (DL) has been very useful and extensively studied in recent times, allowing us to create systems capable of classifying, determining, and/or selecting different types of features within a database. By using these tools in conjunction with advanced image processing available today, work has been developed for a wide range of fields, such as facial recognition, as seen in the work of [23,24], which perform facial recognition using AI. In other cases, it has also been employed for classifications or identifications among individuals; for instance, [25] conducted ethnic identification of participants through the use of DL.…”

Fuzzy Discretization on the Multinomial Naïve Bayes Method for Modeling Multiclass Classification of Corn Plant Diseases and Pests

“…The other name is non-parametric naïve Bayes [30,31]. However, in some cases, these naïve Bayes methods did not obtain the classification performance satisfactorily [5,32], especially in corn plant disease classification [15,16].…”