Sentiment analysis using novel and interpretable architectures of Hidden Markov Models

Perikos, Isidoros; Kardakis, Spyridon; Hatzilygeroudis, Ioannis

doi:10.1016/j.knosys.2021.107332

Cited by 31 publications

(18 citation statements)

References 49 publications

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“…A lexicon is created to rank sentiments that are near to the target word and the results of this model are considered relatively average. One of the recent researches in sentiment analysis is presented in [25] where Hidden Markov Models (HMMs) are used to predict hidden sentiments in the text. The authors aimed to provide different architectures of HMM for training and testing sentiments.…”

Section: Supervised Sentiment Analysismentioning

confidence: 99%

“…English Pos -Neg Deep Learning[17] Using deep learning to analyze Twitter dataset.English 3-Weight Deep Learning[18] Polarity prediction based on supervised learning.Chinese 3-Weight Deep Learning[19] Applying deep learning methods for analyzing sentiments automatically.English Pos -Neg Deep Learning[20] Applying classification using deep learning and machine learning methods.English Pos -Neg Supervised + Deep Learning[21] Merging Bi-LSTM-CNN to analyze sentiments. English 3-Weight Deep Learning[22] Merging LSTM with CNN to analyze reviews.English Pos -Neg Deep Learning[23] Applying CNN algorithm for determining the category or term polarity of the sentence aspect.English 3-Weight Deep Learning[24] Building a sentiment lexicon for five-weight classification using Genetic algorithm.English 5-Weight Genetic Algorithm[25] Applying HMM on sentences for predicting the polarity of sentiments.English 3-Weight Hidden Markov Model[26] Assigning a polarity score for each term to determine the overall polarity of the sentence. Weight Supervised/ Unsupervised[27] Creating a lexicon using Hadoop for storing sentences.…”

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confidence: 99%

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Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

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Sentiment analysis is based on the orientation of user attitudes and satisfaction towards services and subjects. Different methods and techniques have been introduced to analyze sentiments for obtaining high accuracy. The sentiment analysis accuracy depends mainly on supervised and unsupervised mechanisms. Supervised mechanisms are based on machine learning algorithms that achieve moderate or high accuracy but the manual annotation of data is considered a time-consuming process. In unsupervised mechanisms, a lexicon is constructed for storing polarity terms. The accuracy of analyzing data is considered moderate or low if the lexicon contains small terms. In addition, most research methodologies analyze datasets using only 3-weight polarity that can mainly affect the performance of the analysis process. Applying both methods for obtaining high accuracy and efficiency with low user intervention during the analysis process is considered a challenging process. This paper provides a comprehensive evaluation of polarity weights and mechanisms for recent sentiment analysis research. A semi-supervised framework is applied for processing data using both lexicon and machine learning algorithms. An interactive sentiment analysis algorithm is proposed for distributing multi-weight polarities on Arabic lexicons that contain high morphological and linguistic terms. An enhanced scaling algorithm is embedded in the multi-weight algorithm to assign recommended weight polarities automatically. The experimental results are conducted on two datasets to measure the overall accuracy of proposed algorithms that achieved high results when compared to machine learning algorithms.

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Section: Supervised Sentiment Analysismentioning

confidence: 99%

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confidence: 99%

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Mostafa¹

2023

Intelligent Automation &Amp; Soft Computing

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“…In [18] proposed interpretable Hidden Markov Models (HMM)-based approaches for emotion recognition in text and analysed their performance under different architectures (training methods), ordering, and ensembles (e.g.,). The presented models are interpretable; they may show the emotional portions of a phrase and explain the progression of the overall feeling from the beginning to the end of the sentence.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

Reddy¹,

Kasireddy²,

Sirisala³

et al. 2023

Computers, Materials &Amp; Continua

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The rise of social networking enables the development of multilingual Internet-accessible digital documents in several languages. The digital document needs to be evaluated physically through the Cross-Language Text Summarization (CLTS) involved in the disparate and generation of the source documents. Cross-language document processing is involved in the generation of documents from disparate language sources toward targeted documents. The digital documents need to be processed with the contextual semantic data with the decoding scheme. This paper presented a multilingual crosslanguage processing of the documents with the abstractive and summarising of the documents. The proposed model is represented as the Hidden Markov Model LSTM Reinforcement Learning (HMM lstm RL). First, the developed model uses the Hidden Markov model for the computation of keywords in the cross-language words for the clustering. In the second stage, bi-directional long-short-term memory networks are used for key word extraction in the cross-language process. Finally, the proposed HMM lstm RL uses the voting concept in reinforcement learning for the identification and extraction of the keywords. The performance of the proposed HMM lstm RL is 2% better than that of the conventional bi-direction LSTM model.

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“…An integrated framework that integrates sentiment analysis and multi-criteria decision-making methods was employed by Heidary Dahooie [20]. The performance of attention-based models based on RNNs in various sentiment analysis situations was examined by Perikos et al [21]. Ileri and Turan [22] used a neural network for sentiment analysis, and the model's accuracy was roughly 85%.…”

Section: Literature Reviewmentioning

confidence: 99%

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

Punetha

Jain

2023

Appl Intell

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Sentiment Analysis is a method to identify, extract, and quantify people's feelings, opinions, or attitudes. The wealth of online data motivates organizations to keep tabs on customers' opinions and feelings by turning to sentiment analysis tasks. Along with the sentiment analysis, the emotion analysis of written reviews is also essential to improve customer satisfaction with restaurant service. Due to the availability of massive online data, various computerized methods are proposed in the literature to decipher text sentiments. The majority of current methods rely on machine learning, which necessitates the pre-training of large datasets and incurs substantial space and time complexity. To address this issue, we propose a novel unsupervised sentiment classification model. This study presents an unsupervised mathematical optimization framework to perform sentiment and emotion analysis of reviews. The proposed model performs two tasks. First, it identifies a review's positive and negative sentiment polarities, and second, it determines customer satisfaction as either satisfactory or unsatisfactory based on a review. The framework consists of two stages. In the first stage, each review's context, rating, and emotion scores are combined to generate performance scores. In the second stage, we apply a non-cooperative game on performance scores and achieve Nash Equilibrium. The output from this step is the deduced sentiment of the review and the customer's satisfaction feedback. The experiments were performed on two restaurant review datasets and achieved state-of-the-art results. We validated and established the significance of the results through statistical analysis. The proposed model is domain and language-independent. The proposed model ensures rational and consistent results.

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Sentiment analysis using novel and interpretable architectures of Hidden Markov Models

Cited by 31 publications

References 49 publications

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

Enhanced Sentiment Analysis Algorithms for Multi-Weight Polarity Selection on Twitter Dataset

An Efficient Long Short-Term Memory Model for Digital Cross-Language Summarization

Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews

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