Estimating the Rating of the Reviews Based on the Text

Kavousi, Mohammadamir; Saadatmand, Sepehr

doi:10.1007/978-981-13-2514-4_22

Cited by 11 publications

(7 citation statements)

References 37 publications

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“…Hence multi strategy sentiment analysis method with semantic fuzziness has been used to solve the problem. Redhu [31]said every aspect of sentiment analysis using text mining. The discussion has been done to extract information using text mining and sentiment analysis that include data acquisition ,data preprocessing and normalization, feature extraction and representation, labeling and finally the application of various natural language processing and machine learning algorithms.…”

Section: Literature Overviewmentioning

confidence: 99%

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

Arora,

Bansal

2020

IJRTE

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This paper provides a platform for analyzing and summarizing the sentiments expressed by users or customers in the field of online tourism. The objective of this research is to analyze online reviews of all the users to propose a new optimized business model to improve present services of business organization to enhance profit and customer satisfaction. The proposed system filters tourism online reviews and classifies them using sentimental technique with the help of deep learning technique. Deep learning technique will not only identify the polarity of online reviews but also recognizes relevant patterns deeply to find the hidden reviews details. After applying the deep learning technique, the results will be generated through which we can find the inferences. These inferences would provide a great help for improvisation of the subject. In this research a new optimized business model will be implemented using deep learning technique so that we would be able to compare new business model with the present system [1]. The relevance of this research lies in helping tourism industries to understand the social sentiment of their brand, product or service while monitoring online conversations. It helps in enhancing business profits by running online websites throughout by giving best services to the online users or customers.

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Section: Literature Overviewmentioning

confidence: 99%

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

Arora,

Bansal

2020

IJRTE

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“…The major classifiers were Support Vector Machine (SVM) and Multinomial Naive Bayesian (MNB). Callen Rain [6] advocated expanding current Natural Language Processing (NLP) research. To determine whether a review was good or not, the Naive Bayes classifier was used by authors.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Deep Learning Based Sentiment Classification and Product Aspect Analysis

Dhabekar¹,

Patil²

2021

ITM Web Conf.

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With the increase in E-Commerce businesses in the last decade,the sentiment analysis of product reviews has gained a lot of attention in linguistic research. In literature, the survey depicts the majority of the research done emphasizes on mere polarity identification of the reviews. The proposed system emphasized on classifying the sentiment polarity and the product aspect identification from the reviews. Proposed work experimented with traditional machine learning techniques as well as deep neural networks such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory(LSTM) Networks. The proposed system gives a better understanding of these algorithms by comparing the outcomes. The Deep Learning approach in the proposed work successfully provides a mechanism which identifies the review polarity and intensity of the reviews and also analyses the short form words used by people in the reviews. The experimental results in this work, applied on amazon product dataset, shows that the LSTM model works the best for sentiment analysis and intensity of reviews with 93% accuracy. This research work also predicts polarity for short-form word reviews which is the common trend these days while writing the reviews.

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“…Modelling is an effective approach to predict effluent quality parameters of treatment systems and monitor the changes over time. Artificial intelligence and machine learning techniques have shown high capabilities in various fields of science and engineering (Kavousi and Saadatmand, 2018). Neural networks (NNs) are artificial intelligent models, which have been successfully used for monitoring and predicting various parameters in water and wastewater treatment (Mirbagheri et al, 2015; Bagheri et al, 2016a; 2016b).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation and modelling of an integrated municipal wastewater treatment system using neural networks

Mokhtari

Bagheri

Mirbagheri

et al. 2020

Water & Environment J

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This study evaluates and models the impacts of employing biofilm carriers in sequencing batch reactors (SBR). A neural network (NN) was used to predict contaminants in the effluent and analyse the importance of operating parameters. With a hydraulic retention time of 7 h, the removal efficiency of chemical oxygen demand (COD), total phosphorous (TP), and total suspended solids (TSS) were 85, 82, and 98.9%, respectively. The removal efficiency of COD, TP, and TSS in our hybrid system was superior to regular single SBR systems. The training procedure of the NN model was successful and almost a perfect match was achieved between predicted values and experimental values. For all models predicting effluent COD, TP, and TSS, the correlation coefficient was higher than 0.99, and mean squared error approached zero. The analysis of input parameters demonstrated that influent concentration is a significant factor in the modelling of effluent characteristics.

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Estimating the Rating of the Reviews Based on the Text

Cited by 11 publications

References 37 publications

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

Developing a Model for Sentiment Analysis Technique in the field of Tourism using Deep Learning

Implementation of Deep Learning Based Sentiment Classification and Product Aspect Analysis

Performance evaluation and modelling of an integrated municipal wastewater treatment system using neural networks

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