Real-time Sentiment Analysis On E-Commerce Application

Jabbar, Jahanzeb; Urooj, Iqra; Wu, Junsheng; Azeem, Naqash

doi:10.1109/icnsc.2019.8743331

Cited by 48 publications

(20 citation statements)

References 6 publications

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“…A large number of research results have emerged and are widely applied to e-commerce, finance, automobiles, medicine and other industries. 27,28 Real-time sentiment analysis, as a research direction of sentiment analysis technology, can be roughly divided into text-based and image-based analyses. Compared with advertising push and e-commerce, real-time sentiment analysis of text is more reasonable and effective.…”

Section: Real-time Sentiment Analysis Technologymentioning

confidence: 99%

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Tian

Tang

Zhu

et al. 2022

Concurrency and Computation

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Sentiment analysis has a wide application prospect in business, medicine, security and other fields, which provides a new perspective for the development of education. Students' sentiment data play an important role in the evaluation of teachers' teaching quality and students' learning effect, and provide a basis for the implementation of effective learning intervention. However, most of the research is to obtain the real-time learning status of students in the classroom through teachers' naked eye observation and students' text feedback, which will lead to some problems such as incomplete feedback content and delayed feedback analysis. Based on the mini-Xception framework, this article implements the real-time identification and analysis of student sentiment in classroom teaching, and the degree of student engagement is analyzed according to the teaching events triggered by teacher to provide reasonable suggestions for subsequent teaching progress. The experimental results show that the mini-Xception model trained by FER2013 data sets has high recognition accuracy for the real-time detection of seven student sentiments, and the average accuracy is 76.71%. Compared with text feedback, it can assist teachers in understanding student learning states in time so that they can take corresponding actions, and realize the real-time performance of wisdom classroom teaching information feedback, the high efficiency of information transmission, and the intelligence of information processing.

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Section: Real-time Sentiment Analysis Technologymentioning

confidence: 99%

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Tian

Tang

Zhu

et al. 2022

Concurrency and Computation

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show abstract

“…In case of stopwords removal using unigram, random forest outperforms other approaches with 87% accuracy but in other situations, ensemble method gives the best result. [15] This article [16] works on real-time SA of user's review on a product. They used amazon dataset and SVM for designing methodology for experiment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This experiment has been done for both sentence and review level. [16] This article [17] has done research on implicit sentiment. They have evaluated different deep learning techniques on Chinese dataset to find out their performances.…”

Section: Literature Reviewmentioning

confidence: 99%

Online Reviews & Ratings Inter-contradiction based Product’s Quality-Prediction through Hybrid Neural Network

2021

JIEC

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With the explosive growth of web usage, people feel comfort to use internet for their personal work such as shopping, sharing information etc. People are more interested in "what other thinks" so the comments and reviews on any online product, movie has huge effect on its earning. Sentiment analysis help people to judge quality by analyzing the reviews. In this era where everyone is so busy in their routine work, it feels like very difficult and time consuming task to check all available reviews on a product. As reviews on a product can be written by anyone so their certainty can be doubtful for example people can give fake reviews just for enjoyment. Another problem with reviews is that contradiction which exists between ratings and reviews of a product for example a person gives 5 star to a product but write a lengthy list of problem exists in that product. To deals with this problem, we are introducing a methodology that finds out contradiction of reviews and ratings of a product then finds out the actual score/quality of product which can save people's time to read all the reviews or clear their confusion if they stuck between whether to trust on ratings or reviews. In this research we used CNN Hybrid model which gives 97.5% accuracy which is better than previous models. Dataset is collected from amzon.com. We have also applied CNN Hybrid model on different training and testing dataset ratio in both cases i.e. bigger and smaller dataset. We evaluate CNN hybrid model on both smaller and bigger dataset and experimental results conclude that increasing dataset will increase accuracy of CNN Hybrid Model.

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“…Unlike search engines, banking, robotics, 2 e-commerce sites, 3 etc., the app of machine learning in machinery faults classification and diagnosis has not been widely explored. A few instances of its app in fault diagnosis can be found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals

Poddar

Tandon

2021

Proceedings of the Institution of Mechanical Engineers, Part J:

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The ability to classify condition-monitoring data and make a decision can be imparted to a computer through the machine learning approach. In this article, the acoustic emission signals emerging from journal bearings under normal operating conditions and faulty states, namely cavitation, particle contamination and oil starvation, have been classified to develop fault-prediction model using the machine learning approach. Furthermore, an application has been developed that takes acoustic emission data as input and diagnoses the category of faults besides triggering an alarm under faulty states.

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Real-time Sentiment Analysis On E-Commerce Application

Cited by 48 publications

References 6 publications

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Real‐time sentiment analysis of students based on mini‐Xception architecture for wisdom classroom

Online Reviews & Ratings Inter-contradiction based Product’s Quality-Prediction through Hybrid Neural Network

Classification and detection of cavitation, particle contamination and oil starvation in journal bearing through machine learning approach using acoustic emission signals

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