An Efficient Aspect based Sentiment Analysis Model by the Hybrid Fusion of Speech and Text Aspects

Maganti Syamala,N J Nalini

doi:10.14569/ijacsa.2021.0120920

Abstract

Aspect-based Sentiment Analysis (ABSA) is treated to be a challenging task in the domain of speech, as it needs the fusion of acoustic features and Linguistic features for information retrieval and decision making. The existing studies in speech are limited to speech and emotion recognition. The main objective of this work is to combine acoustic features in speech with linguistic features in text for ABSA. A deep learning and language model is implemented for acoustic feature extraction in speech. Different variants of text feature extraction techniques are used for aspect extraction in text. Trained Lexicons, Latent Dirichlet Allocation (LDA) model, Rule based approach and Efficient Named Entity Recognition (E-NER) guided dependency parsing approach has been used for aspect extraction. Sentiment with respect to the extracted aspect is analyzed using Natural Language Processing (NLP) techniques. The experimental results of the proposed model proved the effectiveness of hybrid level fusion by yielding improved results of 5.7% WER and 3% CER when compared with the traditional baseline individual linguistic and acoustic feature models.

Highlights

Sentiment analysis or opinion mining is the area of study in Natural Language Processing (NLP) where it helps to analyze the polarity with respect to the given context
Word Error Rate (WER) and Character Error Rate (CER) are the two-evaluation metrics used for recognizing the performance of the speech recognition model
The proposed speech recognition model proved to improve the efficiency of the model by achieving 5.7% WER and 3% CER

Summary

Introduction

Sentiment analysis or opinion mining is the area of study in NLP where it helps to analyze the polarity with respect to the given context. Feature extraction for sentiment analysis will be differed for different types of input like text, audio and video. The field of sentiment analysis in NLP had gained its popularity by implementing on text. When sentimental analysis came into picture, it‟s been carried out only on text using NLP and machine learning techniques, where the polarity of the given document or sentence is classified as either positive, negative or neutral [1]. Most of the recommender systems that used ABSA have identified the sentiment with respect to the aspect in the given text. Aspect-based sentiment analysis was been carried out by combining both audio and text features

Objectives

Results

Conclusion