Emotion Detection Using Machine Learning Project Report

Emotion Detection Using Machine Learning Thesis Topics

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1. Cascaded Machine Learning-Based Emotion Detection Approach Using EEG Signals

Keywords

Emotion detection, EEG signal, SWT, SVM, RF, Machine learning

            An integrated approach of multi stage support vector machine and a random forest method known as cascaded ML technique is suggested in our study for emotion detection by considering EEG signals. We extracted the features from EEG data by utilizing stationary wavelet transforms. We build the matrix from the extracted features and it is changed into feature vectors. We also compared the findings of CNN and ensemble method.

2. Personalised Emotion Detection from Text Using Machine Learning

Keywords

Text analysis, Word2vec model

            An emotion detection framework is described in our article based on automatic speech recognition (ASR) and text analysis. We developed the suggested model for text automatic analysis by employing PocketSphinx as ASR and Word2vec model, K-means clustering, and TfidfVectoriser. We generated a user dependent framework that assists to detect emotions and to find out effective applications. Results show that, our proposed framework provides greater outcomes.

3. Human Emotion Detection using Machine Learning

Keywords

Visual emotion, Convolutional Neural Networks, Hyperparameters, Psychology

            To accurately detect the human emotions from facial patterns, a CNN based innovative technique is proposed in our study. We employed hyperparameter tweaking techniques like data augmentation to improve the efficiency of the framework. We highlighted the requirement of utilizing ML methods to identify and examine various emotions of human. We also enhanced the utilization of our framework to integrate platforms like effective computing and virtual reality.

4. Machine Learning Algorithm to Detect EEG based Emotion states using Virtual-Video stimuli

Keywords

ANN, KNN

            A new technique is recommended in our study to execute various ML methods for emotion identification. For that, we obtained EEG signals and we extracted various features in terms of time and frequency domains. We utilized 4-level Discrete-Wavelet-Transforms for frequency band decomposition. Several methods such as KNN, SVM, and ANN are applied on the extracted features for categorization of data into various emotions. In that, ANN offers highest results.

5. Emotion Detection from Textual Data Using Supervised Machine Learning Models

Keywords

Emotions, text data, sentiment analysis, classification models, feature engineering, count vectorizer

            A text related emotion identification and forecasting framework creation is the major aim of our research. We evaluated various supervised ML categorization techniques including DT, NB, SVM, LR, KNN, and RF. We performed data preprocessing and it comprises of several procedures like stemming, stop-words, numerals, and punctuation marks elimination, tokenization, and spelling correction. By doing so, we identified various emotions efficiently.

6. Design of subject independent 3D VAD emotion detection system using EEG signals and machine learning algorithms

Keywords

Affective-computing, Discrete-Emotions, DEAP Database, EEG, Subject-Independent, Valence-Arousal-Dominance, Wavelet-based on Atomic-Functions, OPTUNA Hyper-parameter optimization

            A major goal of our research is to build a subject-independent Emotion Detection System (EDS) by considering EEG signals and 3D VAD model. We carried out multi-domain extraction of features by employing Wavelet-based Atomic Function time–frequency method. To minimize data dimensionality and repetition, we utilized PCA method. Various methods like Gradient Boosting, DT and RF are applied on the acquired features. We utilized Optuna to enhance the suggested framework’s efficiency. Here, we implemented three EDS in various perspectives.

7. Detecting Human Emotions through Physiological Signals Using Machine Learning

Keywords

Classification, Physiological signals, Sensors

            A machine learning framework is suggested in our paper to identify several human emotions such as normal, sad, angry and happy. For that, we considered several distinct signals that are acquired from various sensors. We utilized microcontroller to process the signals and it transfers the data to a computer through the USB. Here, we categorized the data by using ML techniques. As a consequence, RF tree method achieved better performance than others.

8. Emotion detection in Arabic text using Machine Learning methods

Keywords

Arabic Text, DT, Naive Bayes

            Emotion identification system is constructed in our research through the employment of ML methods by considering Arabic texts for identification. We classified the texts into various emotions by utilizing supervised ML methods such as Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NB), Multinomial Naive Bayes (NB), and Support Vector Machine (SVM). From the analysis, we conclude that, DT and KNN provide greatest accuracy.

9. Bangla Speech Emotion Detection using Machine Learning Ensemble Methods

Keyword

Bangla

            To enhance the efficiency of emotion recognition by considering speech data retrieved from the audio data, we employed ensemble ML methodologies in our study. Various base classifiers are comprised in the ensemble learning framework. We trained and examined every classifier with both spontaneous and acted emotional speech information. We carried out experimental analysis of our suggested framework with some conventional framework to demonstrate our framework’s efficacy.

10. Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

Keywords

Racism, ensemble, Ukraine-Russia

            A novel approach is proposed in our article to detect emotions and to examine sentiments. We examined the sentiments of Ukraine-Russia war by considering the twitter data through the utilization of unsupervised technique and NLP methods. We employed various ML techniques and ensemble approaches for emotion and racism detection. Finally, we state that, our suggested model outperformed other existing approaches.