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Hierarchical Bi-LSTM based emotion analysis of textual data

Mahto Dashrath, Yadav Subhash Chandra

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2022

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Vol. 70, nr 3

art. no. e141001

EN

Nowadays, Twitter is one of the most popular microblogging sites that is generating a massive amount of textual data. Such textual data is intended to incorporate human feelings and opinions with related events like tweets, posts, and status updates. It then becomes difficult to identify and classify the emotions from the tweets due to their restricted word length and data diversity. In contrast, emotion analysis identifies and classifies different emotions based on the text data generated from social media platforms. The underlying work anticipates an efficient category and prediction technique for analyzing different emotions from textual data collected from Twitter. The proposed research work deliberates an enhanced deep neural network (EDNN) based hierarchical Bi-LSTM model for emotion analysis from textual data; that classifies the six emotions mainly sadness, love, joy, surprise, fear, and anger. Furthermore, the emotion analysis result obtained by the proposed hierarchical Bi-LSTM model is being compared and validated with the traditional hybrid CNN-LSTM approach regarding the accuracy, recall, precision, and F1-Score. It can be observed from the results that the proposed hierarchical Bi-LSTM achieves an average accuracy of 89% for emotion analysis, whereas the existing CNN-LSTM model achieved an overall accuracy of 75%. This result shows that the proposed hierarchical Bi-LSTM approach achieves desired performance compared to the CNN-LSTM model.

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REGA: Real-Time Emotion, Gender, Age Detection Using CNN - A Review

Sharma Dibya Jyoti, Dutta Sachin, Bora Dibya Jyoti

Annals of Computer Science and Information Systems

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2020

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Vol. 24

115--118

EN

In this paper we describe a methodology and an algorithm to estimate the real-time age, gender, and emotion of a human by analyzing of face images on a webcam. Here we discuss the CNN based architecture to design a real-time model. Emotion, gender and age detection of facial images in webcam play an important role in many applications like forensics, security control, data analysis,video observation and human-computer interaction. In this paper we present some method \& techniques such as PCA,LBP, SVM, VIOLA-JONES, HOG which will directly or indirectly used to recognize human emotion, gender and age detection in various conditions.

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Charakterystyki behawioralne w biometrii

Kamińska D., Sapiński T., Pelikant A.

Elektronika : konstrukcje, technologie, zastosowania

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2015

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Vol. 56, nr 10

38-40

PL

W artykule omówiono sposoby pozyskiwania, przetwarzania i reprezentacji sygnałów audio w celu prowadzenia dalszych analiz związanych zarówno z semantyką wypowiedzi, jak również z cechami behawioralnymi mówcy. Przyjęto, że analiza danych powinna być prowadzona możliwie blisko miejsca ich przechowywania, np. w komercyjnych serwerach baz danych z wykorzystaniem enkapsulacji klas obiektowych do elementów programistycznych relacyjnego serwera. Poza wykorzystaniem reprezentacji sygnału za pomocą wektorów wyrażonych w skalach cepstralnych, ważnym elementem analizy jest zastosowanie algorytmów dopasowania strumieni wektorów danych – Spring DTW. W przypadku analizy stanów emocjonalnych do wzmocnienia procesu klasyfikacji zastosowano komitety klasyfikatorów działających na różnych zestawach atrybutów, a analizę odniesiono do modelu Plutchika.

EN

The article describes methods of acquisition, processing and representation of audio signals for the purpose of further analysis associated with both the semantics of expression, as well as behavioral characteristics of the speaker. It is assumed that the data analysis should be carried out as close to the place of storage, eg. in commercial database servers using the encapsulation of object classes to relational server software components. In addition to using a representation of a signal as vectors in cepstral scale, an important part of the analysis is to apply matching algorithms - Spring DTW. In order to enhance the analysis of emotional states classification committees consiting of classifiers operating on different sets of attributes were used. Emotion detection was based on Plutchik’s wheel.