KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

Abstract

The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.

An Analysis of French-Language Tweets About COVID-19 Vaccines: Supervised Learning Approach

“…For example, sentiment analyses were conducted on messages posted on Twitter (tweets) to measure the opinions of Americans regarding vaccines [ 12 ] and evaluate the rate of hate tweets among Arab people [ 13 ]. Additionally, another method, opinion mining, is used and has obtained an equal level of maturity [ 14 ]. Both methods attempt to identify and categorize subjective content in text, but it is not an easy task to correctly identify such concepts (opinion, rumor, idea, claim, argument, emotion, sentiment, and affect).…”

Classification of Twitter COVID Tweets Using Deep CNN-SLSTM Technique

“…The output of level 0 LSTM is transferred to the level 1 LSTM layer for the prediction of new data. The calculation performed at level 0 is described below by equation [1][2][3][4][5][6].…”

“…Opinion mining or sentiment analysis is a type of data prediction that uses short statements or tweets obtained from social media or online review websites [1,2]. Researchers are now using a variety of natural language processing approaches, machine learning, and deep learning classifiers to predict the opinion or comment from the text.…”

Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media

“…In this technique, a generalized neural tensor block is used along with two-channel classifiers to perform sentiment classification and contextual compositionality correspondingly. In the study conducted earlier [16], a novel reference method was proposed for SA i.e., Aspect-Based Sentiment Classification (ABSA) abbreviated as KnowMIS-ABSA approach. This method deals the data with a deliberation that opinion, sentiment, affect, and emotion are dissimilar conceptions and it is incorrect to utilize similar metrics and methods to evaluate them.…”