Lexicon-based sentiment analysis on movie review in the Gujarati language

Shah, Parita; Swaminarayan, Priya

doi:10.1504/ijitcc.2021.119111

Cited by 1 publication

(1 citation statement)

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“…In the first category, we can find approaches based on: frequency or statistics (Hu and Liu 2004;Bafna and Toshniwal 2013;Rana and Cheah 2018;Luo et al 2015); heuristics like (Singh et al 2013) or the work of (Bancken et al 2014) that uses a syntactic dependency path to identify entities or (Poria et al 2014) that adopts a rule-based approach. In the semi-supervised category, we can find techniques based on lexicon (Yan et al 2015;D'Aniello et al 2018;Shah and Swaminarayan 2021;Klyuev and Oleshchuk 2011) or dependency trees (Yu et al 2011) and graphs (Xu et al 2013). Supervised techniques typically use machine learning approaches like random field, SVM (Manek et al 2017), decision trees, neural networks, and autoencoders (Angelidis and Lapata 2018;Tomasiello 2020).…”

Section: Aspect-based Sentiment Analysismentioning

confidence: 99%

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

D’Aniello

Rocca

2022

Artif Intell Rev

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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.

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