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Performance of Product Review Prediction based on CNN and SVM Techniques
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 185 - Number 24 |
| Year of Publication: 2023 |
| Authors: Rashi Zope, Sachin Patel |
10.5120/ijca2023922992
|
Rashi Zope, Sachin Patel . Performance of Product Review Prediction based on CNN and SVM Techniques. International Journal of Computer Applications. 185, 24 ( Jul 2023), 15-21. DOI=10.5120/ijca2023922992
Abstract
Sentiment analysis and opinion mining are two of the most well-known fields that use text data from places like Facebook, Twitter, Amazon, and other places to evaluate and learn about people. It is important because it lets businesses actively work on improving their business plans and fully understand what customers say about their goods. It is also important because it is important. In this process, you need to do both computational analysis of a person's buying habits and opinion mining about a company's business structure. This thing could be a person, a place, a blog post, or the experience of using a tool. Reviews, ratings, and comments left by users can be examined to provide more insightful data for business usage. Understanding the consumer's needs and foretelling their future intents towards the service are made possible through the analysis of such consumer behaviour. This cognitive research on e-commerce Businesses can monitor how their items are used and how customers feel about them, then use the information to develop a personalised shopping experience for their customers and boost organisational profit. The dataset for this study's product reviews was obtained from a dataset source. The pre-processing processes must then be put into practise. Next, NLP approaches are used to construct the system. The machine learning SVM and CNN-based customer review prediction system offers a strong method for analysing and forecasting the sentiment of customer reviews. This system uses SVM and CNN algorithms for classification, data pre-processing, data splitting, and result creation. In the pre-processing stage, the text input is cleaned up and transformed using NLP techniques, while missing values are handled and labels are encoded. To train the models and assess their effectiveness, the data is then divided into training and testing sets. The classification process uses the SVM and CNN algorithms, with CNN utilising convolutional layers for feature extraction and SVM using hyperplanes for data separation. The trained models are assessed by the system using metrics like recall, precision, and accuracy to produce results.
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