Extraction-Based Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method

Yadav, Nisha; Kumar, Rajeev; Gour, Bhupesh; Khan, Asif

doi:10.1109/wocn45266.2019.8995164

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…In [3], the objective is to develop a hybrid model for sentiment analysis using convolutional neural network-long short-term memory (CNN-LSTM). The model is designed with dropout, max pooling, and batch normalization techniques to improve its performance.…”

Section: Detailed Literaturementioning

confidence: 99%

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Online Reviews Classification and Summarization: A Comprehensive Analysis

Abdullah,

Ashraf,

Jhanjhi

2023

Preprint

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The classification and summarization of online reviews using machine learning and deep learningbased approaches are the subject of this systematic literature review. Consumers, companies, and researchers can now find vital information from online reviews. Automatically categorizing and summarizing these reviews can offer useful information and facilitate decision-making. The purpose of this study is to offer a thorough overview of the current methodology, approaches, and developments in this area. The review concentrates on machine learning-based methods for categorization and summarizing tasks, highlighting their advantages, drawbacks, and new developments. This systematic review provides insights into the most recent methodologies and indicates prospective directions for further investigation by synthesizing the data from a wide range of pertinent research publications

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Section: Detailed Literaturementioning

confidence: 99%

“…In [3], novel hybrid classification model is proposed that is based on coupling of classification methods. The Classifier Collection was constructed using Naïve Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA).…”

Section: Critical Analysismentioning

confidence: 99%

Online Reviews Classification and Summarization: A Comprehensive Analysis

Abdullah,

Ashraf,

Jhanjhi

2023

Preprint

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“…In the third sentiment classification method of ABSA, Santhosh et al [43] used POS tag for aspect-opinion pair formation, and final polarity detection. Nisha et al [44] used a hybrid with unsupervised ML method LDA with lexicon. Kushal et al [45] used text formatting with fuzzy matching and domain grouping of synonym words, aspect extraction with POS tagging, association mining and probabilistic approach, opinion extraction with words extraction, and final polarity detection for aspect-opinion pair, and aspect-based summarization.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Syntax Dependency with Lexicon and Logistic Regression for Aspect-based Sentiment Analysis

Kabir,

Othman,

Yaakub

et al. 2023

IJACSA

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Aspect-based Sentiment Analysis (ABSA) is a finegrained form of SA that greatly benefits customers and the real world. ABSA of customer reviews has become a trendy topic because of the profuse information that is shared through these reviews. While SA also known as opinion mining helps to find opinion, ABSA greatly impact business world by converting these reviews to finer form with aspects and opinion or sentiment. These review words are interwoven internally, which depends on the semantics besides syntax, and sometimes there are long dependencies. Recently, the hybrid methods for ABSA are popular, but most of them merely considered if the syntax and long dependency exist, thus missing the inclusion of multi and infrequent aspects. In addition, in most literature, sentiment classification is shown directly without calculating the sentiment scores in ABSA. To this effect, this paper proposes a hybrid with syntax dependency and the lexicon for aspect, sentiment extraction, and polarity classification by Logistic Regression (LR) classifier to overcome the issues in ABSA. The proposed method is able to address the challenges of ABSA in a number of ways. First, it is able to extract multi-word and infrequent aspects by using syntactic dependency information. Second, it is able to calculate sentiment scores, which provides a more nuanced understanding of the overall sentiment expressed towards an aspect. Third, it is able to capture long dependencies between words by using syntactic dependency and semantic information. The proposed hybrid model outperformed the other methods by an average of 8-10 percent with the standard public dataset in terms of accuracy.

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Sentiment Analysis of Customers Review Using Hybrid Approach

Budhwar

Singh

2022

Communications in Computer and Information Science

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Extraction-Based Text Summarization and Sentiment Analysis of Online Reviews Using Hybrid Classification Method

Cited by 6 publications

References 1 publication

Online Reviews Classification and Summarization: A Comprehensive Analysis

Online Reviews Classification and Summarization: A Comprehensive Analysis

Hybrid Syntax Dependency with Lexicon and Logistic Regression for Aspect-based Sentiment Analysis

Sentiment Analysis of Customers Review Using Hybrid Approach

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