A survey on sentiment classification algorithms, challenges and applications

Rana, Muhammad Rizwan Rashid; Nawaz, Asif; Iqbal, Javed

doi:10.2478/ausi-2018-0004

Cited by 9 publications

(6 citation statements)

References 32 publications

(20 reference statements)

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“…Типична следующая позиция: «Мы идентифицируем методы обнаружения настроений как принадлежащие к одной из трех категорий, каждая из которых имеет свои достоинства и недостатки: словарные методы, методы машинного обучения с учителем и методы машинного обучения без учителя» [Reagan et al 2017]. Доста точно популярна точка зрения, согласно которой выделяют модели машинного обучения, словарную и гибридную [Poria et al 2020;Rana et al 2018]. Здесь не упомянута Однако основанное на правилах моделирование так или иначе признается актуальным, о чем все чаще говорится прямо [Пазельская, Соловьев 2011;Taboada et al 2011].…”

Section: анализ и результатыunclassified

Sentiment Analysis: Linguistic Potential of Preprocessing Regimentation

Баркович

2023

Virtual Communication and Social Networks

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The article deals with the sentiment analysis regimentation as a relevant direction in automated natural language processing and its linguistic potential. Despite its impressive practical significance, the sentiment analysis still lacks reliable theoretical foundation. Although information technologies develop very fast, their fundamental foundations correlate with the linguistic system of knowledge. In fact, the methodological priority of the applied linguistics has no alternative with regard to the interdisciplinary specificity of the modern communication. The complex nature of this research made the authors appeal to the computer linguistics in order to provide a meta-description on the algorithmization and modeling of sentiment evaluation. The effectiveness of the relevant practice was conditioned by the optimal configuration of the procedure and an appropriate material evaluation. The preprocessing included identifying the meta-structure, defining its referentiality and level orientation, and choosing the analysis model. The authors described these main steps of the preprocessing algorithm, as well as the relevant practice. The study contributes to productive theoretical optimization of text sentiment analysis. In a broad context, the expedient disclosure of linguistic potential is relevant to the whole sphere of automated natural language processing.

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Section: анализ и результатыunclassified

Sentiment Analysis: Linguistic Potential of Preprocessing Regimentation

Баркович

2023

Virtual Communication and Social Networks

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“…Sentiment classification, as a branch of natural language processing study, is the process of exploring emotions, ideas and intrinsic thoughts in the commented review items (Rana et al, 2018). The survey-based or questionnaire-oriented research design has proven to be effective to scrutinize the consumer behavior, where the pre-design on scale and item in a structured way is the prerequisite of the empirical study (Dailey and Ulku, 2018;Heesen and Madlener, 2018;Kraft et al, 2018).…”

Section: Sentiment Classificationmentioning

confidence: 99%

“…In practical, there are multi-state emotions of product consumers, and a few researches can reflect multi-grade sentiment. However, most publications on sentiment analysis focused on polarity classification (Asgarian et al, 2018;Rana et al, 2018).…”

Section: Sentiment Classificationmentioning

confidence: 99%

What attracts vehicle consumers’ buying

Zhou

Lim

et al. 2019

IMDS

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Purpose The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

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“…Social media has become a key source of knowledge on various subjects because of its role as a platform for information exchange [1]. Media resources posted on social media sites, such as YouTube, Facebook and Twitter, are known as "social multimedia" [2].…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

Rana¹,

Rehman²,

Nawaz³

et al. 2023

Computer Systems Science and Engineering

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People utilize microblogs and other social media platforms to express their thoughts and feelings regarding current events, public products and the latest affairs. People share their thoughts and feelings about various topics, including products, news, blogs, etc. In user reviews and tweets, sentiment analysis is used to discover opinions and feelings. Sentiment polarity is a term used to describe how sentiment is represented. Positive, neutral and negative are all examples of it. This area is still in its infancy and needs several critical upgrades. Slang and hidden emotions can detract from the accuracy of traditional techniques. Existing methods only evaluate the polarity strength of the sentiment words when dividing them into positive and negative categories. Some existing strategies are domain-specific. The proposed model incorporates aspect extraction, association rule mining and the deep learning technique Bidirectional Encoder Representations from Transformers (BERT). Aspects are extracted using Part of Speech Tagger and association rule mining is used to associate aspects with opinion words. Later, classification was performed using BER. The proposed approach attained an average of 89.45% accuracy, 88.45% precision and 85.98% recall on different datasets of products and Twitter. The results showed that the proposed technique achieved better than state-of-the-art sentiment analysis techniques.

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A survey on sentiment classification algorithms, challenges and applications

Cited by 9 publications

References 32 publications

Sentiment Analysis: Linguistic Potential of Preprocessing Regimentation

Sentiment Analysis: Linguistic Potential of Preprocessing Regimentation

What attracts vehicle consumers’ buying

Aspect-Based Sentiment Analysis for Social Multimedia: A Hybrid Computational Framework

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