HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Kaur, Gagandeep; Sharma, Amit

doi:10.1007/s12652-022-03748-6

Cited by 5 publications

(7 citation statements)

References 47 publications

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“…Using this method, aspect-related information can be extracted without utilizing ML techniques requiring high-processing computations. Algorithm 2 is illustrated in [54] with a sample example. The hybrid feature vector is then constructed using the RRF and ARF vectors without losing generality.…”

Section: End 31 Endmentioning

confidence: 99%

“…5, 6, and 7 show the comparative analysis of the proposed SADL model with state-of-the-art methods utilizing datasets SemEval-2014, Sentiment140, and STS-Gold datasets, respectively. The various recent SA methods that have been used for investigation include supervised ABSA (SABSA) [28], SentiVec [21], TF-IDF+N-gram+SVM [25], SEML [37], MTMVN [33], and hybrid analysis of sentiments (HAS) [54]. We assessed the performance of proposed and existing methods based on precision, recall, and F1-score parameters.…”

Section: Sa Analysis Using ML and Dl-based Classifiersmentioning

confidence: 99%

“…Table 5. Comparison of SADL with state-of-the-art SA approaches utilizing SemEval-2014 dataset SA/ABSA approach Precision Recall F1-score SABSA [28] 0.844 0.831 0.838 SentiVec [21] 0.862 0.842 0.854 TF-IDF+N-gram+SVM [25] 0.851 0.838 0.845 SEML [37] 0.841 0.824 0.833 MTMVN [33] 0.793 0.773 0.785 HAS [54] 0.946 0.912 0.922 SADL 0.956 0.954 0.945 Table 6. Comparison of SADL with state-of-the-art SA approaches utilizing Sentiment140 dataset SA/ABSA approach Precision Recall F1-score SABSA [28] 0.858 0.839 0.8485 SentiVec [21] 0.877 0.858 0.8675 TF-IDF+N-gram+SVM [25] 0.866 0.846 0.856 SEML [37] 0.854 0.837 0.8455 MTMVN [33] 0.817 0.789 0.803 HAS [54] 0.961 0.938 0.9495 SADL 0.963 0.965 0.956 Table 7.…”

Section: Sa Analysis Using ML and Dl-based Classifiersmentioning

confidence: 99%

“…Comparison of SADL with state-of-the-art SA approaches utilizing SemEval-2014 dataset SA/ABSA approach Precision Recall F1-score SABSA [28] 0.844 0.831 0.838 SentiVec [21] 0.862 0.842 0.854 TF-IDF+N-gram+SVM [25] 0.851 0.838 0.845 SEML [37] 0.841 0.824 0.833 MTMVN [33] 0.793 0.773 0.785 HAS [54] 0.946 0.912 0.922 SADL 0.956 0.954 0.945 Table 6. Comparison of SADL with state-of-the-art SA approaches utilizing Sentiment140 dataset SA/ABSA approach Precision Recall F1-score SABSA [28] 0.858 0.839 0.8485 SentiVec [21] 0.877 0.858 0.8675 TF-IDF+N-gram+SVM [25] 0.866 0.846 0.856 SEML [37] 0.854 0.837 0.8455 MTMVN [33] 0.817 0.789 0.803 HAS [54] 0.961 0.938 0.9495 SADL 0.963 0.965 0.956 Table 7. Comparison of SADL with state-of-the-art SA approaches utilizing STS-Gold dataset SA/ABSA approach Precision Recall F1-score SABSA [28] 0.791 0.784 0.7875 SentiVec [21] 0.845 0.837 0.841 TF-IDF+N-gram+SVM [25] 0.831 0.817 0.824 SEML [37] 0.826 0.809 0.8175 MTMVN [33] 0.776 0.758 0.767 HAS [54] 0.927 0.899 0.913 SADL 0.954 0.947 0.951…”

Section: Sa Analysis Using ML and Dl-based Classifiersmentioning

confidence: 99%

“…The suggested ARF mechanism with illustrations is presented in our recent publication [54] and is outlined in Algorithm 2 as well.…”

mentioning

confidence: 99%

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Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Kaur,

Sharma

2024

IJECE

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Customer review summarization (CRS) offers business owners summarized customer feedback. The functionality of CRS mainly depends on the sentiment analysis (SA) model; hence it needs an efficient SA technique. The aim of this study is to construct an SA model employing deep learning for CRS (SADL-CRS) to present summarized data and assist businesses in understanding the behavior of their customers. The SA model employing deep learning (SADL) and CRS phases make up the proposed automatic SADL-CRS model. The SADL consists of review preprocessing, feature extraction, and sentiment classification. The preprocessing stage removes irrelevant text from the reviews using natural language processing (NLP) methods. The proposed hybrid approach combines review-related features and aspect-related features to efficiently extract the features and create a unique hybrid feature vector (HF) for each review. The classification of input reviews is performed using a deep learning (DL) classifier long short-term memory (LSTM). The CRS phase performs the automatic summarization employing the outcome of SADL. The experimental evaluation of the proposed model is done using diverse research data sets. The SADL-CRS model attains the average recall, precision, and F1-score of 95.53%, 95.76%, and 95.06%, respectively. The review summarization efficiency of the suggested model is improved by 6.12% compared to underlying CRS methods.

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Section: End 31 Endmentioning

confidence: 99%

Section: Sa Analysis Using ML and Dl-based Classifiersmentioning

confidence: 99%

Section: Sa Analysis Using ML and Dl-based Classifiersmentioning

confidence: 99%

Section: Sa Analysis Using ML and Dl-based Classifiersmentioning

confidence: 99%

“…The suggested ARF mechanism with illustrations is presented in our recent publication [54] and is outlined in Algorithm 2 as well.…”

mentioning

confidence: 99%

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Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Kaur,

Sharma

2024

IJECE

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GCMK: Detecting Spam Movie Review Based on Graph Convolutional Network Embedding Movie Background Knowledge

Cao

et al. 2022

Lecture Notes in Computer Science

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A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis

Parvin,

Sumathi,

Barani

2023

IJST

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Objectives:The objective of this study is to introduce an innovative hybrid approach that incorporates CNN and Bi-LSTM models to provide a solution to the sentiment analysis problem. The HCNN-BiLSTM Model is the acronym that we present for this methodology. Methods: Pre-processing, feature extraction, and sentiment classification are the three steps in this procedure. In the pre-processing stage, unneeded data gathered from the source text reviews is filtered out utilizing NLP systems. The prior studies presented an integrated strategy referred to as RBDT, which generates particular feature sets depending on the examination, for effectively extracting features. Next, sentiments are predicted using the proposed cutting-edge HCNN-BiLSTM model and grouped various sentimental phrases into five main groups: interest, sadness, anger, happiness, and disinterest. Findings: The findings showed that in terms of F-measure, accuracy, word count, and computational time, this suggested the HCNN-BiLSTM Model operates better than conventional deep learning (CNN) and machine learning techniques (SVM). Novelty: This proposed approach uses advanced methods on five review datasets, which include the Amazon dataset, Spotify app reviews, FIFA World Cup reviews, COVID-19 Vaccination reviews, and ChatGPT reviews, to produce competitive outcomes.

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HAS: Hybrid Analysis of Sentiments for the perspective of customer review summarization

Cited by 5 publications

References 47 publications

Automatic customer review summarization using deep learning-based hybrid sentiment analysis

Automatic customer review summarization using deep learning-based hybrid sentiment analysis

GCMK: Detecting Spam Movie Review Based on Graph Convolutional Network Embedding Movie Background Knowledge

A Novel Approach to Classify Sentiments on Different Datasets Using Hybrid Approaches of Sentiment Analysis

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