Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach

Nasfi, Rim; Bouguila, Nizar

doi:10.1007/978-3-031-23028-8_8

Cited by 7 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…SVMs and logistic regression are still used, although sophisticated methods are becoming more popular. Deep learning models, such as deep neural networks, and cutting-edge transformers like BERT and LSTM, have shown their ability to capture complex textual relationships [14,24,29,38,39,43,110,112]. The use of cutting-edge algorithms like CNN and CF diversifies sentiment analysis [38,61].…”

Section: Discussionmentioning

confidence: 99%

“…The findings helped merchants improve their products and services and provided users with personalized recommendations. A mixed generativediscriminative strategy combining Fisher kernels and hidden Markov models improved textual sentiment analysis [43]. Amazon and IMDb user reviews showed that the method improved sentiment identification compared to established methods.…”

Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

See 1 more Smart Citation

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

View full text Add to dashboard Cite

In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Sentiment Analysis and Opinion Miningmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…However, sentiment analysis involves analyzing the content and meaning of text to extract sentiments, rather than modeling sequential patterns. Therefore, Gaussian HMM is not effective for sentiment analysis tasks that focus on extracting sentiments from text data 37 …”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Therefore, Gaussian HMM is not effective for sentiment analysis tasks that focus on extracting sentiments from text data. 37 However, the strength of our proposed model lies in the utilization of continuous feature values obtained from our 1D CNN, which correspond to a feature sentence matrix from a dense layer. These continuous feature values can be effectively utilized as inputs to a Gaussian HMM.…”

Section: Architecture Of Incorporating Cnn To Hmmmentioning

confidence: 99%

Sentiment analysis incorporating convolutional neural network into hiddenMarkovmodel

Najafabadi

2024

Computational Intelligence

View full text Add to dashboard Cite

The analysis of sentiments and mining of opinions have become more and more important in years because of the development of social media technologies. The methods that utilize natural language processing and lexicon‐based sentiment analysis techniques to analyze people's opinions in texts require the proper extraction of sentiment words to ensure accuracy. The current issue is tackled with a novel perspective in this paper by introducing a hybrid sentiment analysis technique. This technique brings together Convolutional Neural Network (CNN) and Hidden Markov Models (HMMs), to accurately categorize text data and pinpoint feelings. The proposed method involves 1D convolutional‐layer CNN to extract hidden features from comments and applying HMMs on a feature‐sentence matrix, allowing for the utilization of word sequences in extracting opinions. The method effectively captures diverse text patterns by extracting a range of features from texts using CNN. Text patterns are learned using text HMM by calculating the probabilities between sequences of feature vectors and clustering feature vectors. The paper's experimental evaluation employs benchmark datasets such as CR, MR, Subj, and SST2, demonstrating that the proposed method surpasses existing sentiment analysis techniques and traditional HMMs. One of its strengths is to analyze a range of text patterns and identify crucial features that recognize the emotion of different pieces of a sentence. Additionally, the research findings highlight the improved performance of sentiment analysis tasks through the strategic use of zero padding in conjunction with the masking technique.

show abstract

“…With the help of these characteristics, the Heuristic Deep Neural Network (HDNN) is used for classification, and the proposed FOA and WOA are used to tweak the DNN's parameters for the highest accuracy rate. Nasfi et al [21] used a hybrid generative-discriminative technique that combined Fisher kernels with generalized inverted Dirichlet-based Hidden Markov Models (HMM) to enhance recognition performance in textual analysis. They provide a technique that combines SVM's discriminative approach with generative HMMs.…”

Section: And DL Approaches To Sentiment Analysismentioning

confidence: 99%

Analysis of customer reviews with an improved VADER lexicon classifier

Barik,

Misra

2024

J Big Data

View full text Add to dashboard Cite

Background The importance of customer reviews in determining satisfaction has significantly increased in the digital marketplace. Using sentiment analysis in customer reviews has immense potential but encounters challenges owing to domain heterogeneity. The sentiment orientation of words varies by domain; however, comprehending domain-specific sentiment reviews remains a significant constraint. Aim This study proposes an Improved VADER (IVADER) lexicon-based classification model to evaluate customer sentiment in multiple domains. The model involves constructing a domain-specific dictionary based on the VADER lexicon and classifying doeviews using the constructed dictionary. Methodology The proposed IVADER model uses data preprocessing, Vectorizer transformation, WordnetLemmatizer-based feature selection, and enhanced VADER Lexicon classifier. Result Compared to existing studies, the IVVADER model accomplished outcomes of accuracy of 98.64%, precision of 97%, recall of 94%, f1-measure of 92%, and less training time of 44 s for classification. Outcome Product designers and business organizations can benefit from the IVADER model to evaluate multi-domain customer sentiment and introduce new products in the competitive online marketplace.

show abstract

Sentiment Analysis from User Reviews Using a Hybrid Generative-Discriminative HMM-SVM Approach

Cited by 7 publications

References 17 publications

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Sentiment analysis incorporating convolutional neural network into hiddenMarkovmodel

Analysis of customer reviews with an improved VADER lexicon classifier

Contact Info

Product

Resources

About