A Speech-based Sentiment Analysis using Combined Deep Learning and Language Model on Real-Time Product Review
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2021 by IJETT Journal|
|Year of Publication : 2021|
|Authors : Maganti Syamala, N.J.Nalini
|DOI : 10.14445/22315381/IJETT-V69I1P226|
MLA Style: Maganti Syamala, N.J.Nalini. "A Speech-based Sentiment Analysis using Combined Deep Learning and Language Model on Real-Time Product Review" International Journal of Engineering Trends and Technology 69.1(2021):172-178.
APA Style:Maganti Syamala, N.J.Nalini. A Speech-based Sentiment Analysis using Combined Deep Learning and Language Model on Real-Time Product Review International Journal of Engineering Trends and Technology, 69(1), 172-178.
Sentiment analysis is the area of study in Natural Language Processing (NLP), where it has gained its popularity in text analytics for making any kind of purchase decision. Also, there is a need for speech-based sentiment analysis in real-world applications for providing a better quality of service. But the work carried out in the speech domain has gained very less attention. So, this paper proposed a speech sentiment analysis model by considering spectrogram as an acoustic feature. The spectrogram features are trained over a deep learning model and an N-gram Language model. A combined Convolutional Neural Network (CNN) and Bi-directional-Recurrent Neural Network (Bi-RNN) architecture frameworks are implemented for acoustic modeling and a bi-gram language model to calculate the likelihood of a particular word sequence from the spoken utterance. NLP techniques like the Vader Sentiment Intensity Analyzer function is used for performing the sentiment analysis. The experimental results are analyzed in terms of Word Error Rate (WER) and Character Error Rate (CER) and proved that the proposed model holds outperforming WER and CER of 5.7% and 3 % when compared with the traditional Automatic Speech Recognition (ASR) models. The obtained sentiment analysis results are measured using correctly classified instances, precision, recall, and f1-score using various machine learning algorithms. The logistic Regression algorithm proved to achieve improved accuracy of 90% with the proposed speech sentiment analysis model.
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Acoustic, Character Error Rate, Convolutional Neural Network, Machine Learning, Natural language processing, Recurrent Neural Network, Speech, Spectrogram, Sentiment analysis, Word Error Rate.