HOMPer: A new hybrid system for opinion mining in the Persian language

Basiri, Mohammad Ehsan; Kabiri, Arman

doi:10.1177/0165551519827886

Cited by 30 publications

(12 citation statements)

References 41 publications

(78 reference statements)

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“…To classify the textual viewers' comments using a supervised method, each comment should be first converted into a feature vector. It has been shown that n-gram features, TF-IDF, part of speech (POS), and lexical features are effective features for emotion recognition and sentiment analysis [22], [80]. The input textual comments are first preprocessed, then features are extracted from the preprocessed texts.…”

Section: Textual Feature Extractionmentioning

confidence: 99%

“…Low-order N-grams including unigram and bigram are used broadly in NLP tasks to capture textual context [80]- [82]. It has been shown that the combination of unigram and bigram features are effective for sentiment analysis [22]. TF-IDF is also a popular feature extraction scheme in NLP and text mining tasks.…”

Section: Textual Feature Extractionmentioning

confidence: 99%

“…Textual modality may be in the form of either the movie transcript [20], [21] or the user comments in social networks [14], [15], [22]. An obvious distinction between these two types of textual modalities is their nature.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

et al. 2019

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Multimodal emotion recognition is an emerging interdisciplinary field of research in the area of affective computing and sentiment analysis. It aims at exploiting the information carried by signals of different nature to make emotion recognition systems more accurate. This is achieved by employing a powerful multimodal fusion method. In this study, a hybrid multimodal data fusion method is proposed in which the audio and visual modalities are fused using a latent space linear map and then, their projected features into the cross-modal space are fused with the textual modality using a Dempster-Shafer (DS) theory-based evidential fusion method. The evaluation of the proposed method on the videos of the DEAP dataset shows its superiority over both decision-level and non-latent space fusion methods. Furthermore, the results reveal that employing Marginal Fisher Analysis (MFA) for feature-level audio-visual fusion results in higher improvement in comparison to cross-modal factor analysis (CFA) and canonical correlation analysis (CCA). Also, the implementation results show that exploiting textual users' comments with the audiovisual content of movies improves the performance of the system.

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Section: Textual Feature Extractionmentioning

confidence: 99%

Section: Textual Feature Extractionmentioning

confidence: 99%

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A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

et al. 2019

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“…Basiri et al [23] exploited the DS theory for predicting the sentiment of users from their comments and Nemati and Naghsh-Nilchi [24,25] used DS theory in multimodal affective video retrieval. More recently, Basiri and Kabiri [26] used the DS theory for aggregating sentiment labels and combining supervised and unsupervised machine learning classifier results for sentiment analysis [27]. The remainder of the article is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence

2019

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One of the goals of sustainable development is to achieve economic and social growth according to environmental criteria. Nowadays, impact assessment is an efficient decision making method in planning and management with environmental perspectives. Environmental risk assessment is a tool to reduce the impacts and consequences of various activities on the environment in order to achieve sustainable development. One of the commonly used environmental risk assessment methods is the probability–impact matrix method, which is known as a quantitative method for risk assessment of projects. In this method, numerical estimates of probability and impact of risk occurrence are very difficult, and these factors are associated with uncertainty. When uncertainty exists, data integration is of great importance, for which the fuzzy inference system and evidence theory are known as effective methods. Unavailability of experts’ opinion and the exponential growth of the number of required fuzzy rules associated with the risk factors are two drawbacks of fuzzy inference. Dempster–Shafer’s theory of evidence is one of the popular theories used in intelligent systems for modeling and reasoning under uncertainty and inaccuracy. In this paper, an evidential model for project environmental risk assessment is proposed based on the Dempster–Shafer theory, which is capable of taking into account the uncertainties. The proposed model is used to assess the environmental risks of Maroon oil pipelines in Isfahan. In addition, the proposed model is used in the case of tunneling risk assessment taken from the subject literature. To evaluate the validity of the proposed evidential model, the results are compared in two case studies, with the results of the conventional risk assessment method and the fuzzy inference system method. The comparative results show that the proposed model has a high potential for project risk assessment under an uncertain environment.

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“…Tian et al have used a heuristic approach for scheduling of virtual machine reservations in cloud data centres [10]. Basiri and Kabiri developed a machine learning-based approach for opinion mining, a subfield of data mining [11]. Sharma et al presented a genetic algorithm (GA) and ontology-based NLP frameworks for online opinion mining [12].…”

Section: Introductionmentioning

confidence: 99%

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

Mane

Adamuthe

Omane³

2018

ICST Transactions on Scalable Information Systems

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INTRODUCTION: The teaching-learning based optimization (TLBO) algorithm is a recently developed algorithm. The proposed work presents a design of a master-slave TLBO algorithm. OBJECTIVES: This research aims to design a master-slave TLBO algorithm to improve its performance and system utilization for CEC2006 single-objective benchmark functions. METHODS: The proposed approach implemented using OpenMP and CUDA C, a hybrid programming approach to enhance the utilization of the system's computational resources. The device utilization and performance of the proposed approach evaluated using CEC2006 benchmark functions. RESULTS: The proposed approach obtains best results in significantly reduced time for CEC2006 benchmark functions. The maximum speed-up achieved is 30.14X. The average GPGPU utilization is 90% and the average utilization of logical processors is more than 90%. CONCLUSION: The master-slave TLBO algorithm improves the utilization of computational resources significantly and obtains the best results for CEC2006 benchmark functions.

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HOMPer: A new hybrid system for opinion mining in the Persian language

Cited by 30 publications

References 41 publications

A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

A Hybrid Latent Space Data Fusion Method for Multimodal Emotion Recognition

An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence

Master-Slave TLBO Algorithm for Constrained Global Optimization Problems

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