Abstract
Product Market Analysis understands how the market reacts to a product manufactured by a company. In this paper, a deep-learning-based model is created. The model can understand how a customer feels about a particular product. The dataset used is “fer2013” (Ref. Kaggle Dataset) and is famous for creating “Sentiment Analysis.” The model developed is a self-made model giving a training accuracy of 68.61 and 65.92% test accuracy. The self-made model is a 27-layer deep convolutional neural network consisting of 8 convoluting layers, three max-pooling layers, and two fully connected layers. The model is created using Keras, which is a framework built on Tensorflow, a machine learning library. A total of 427,319 parameters are used to develop the proposed model. Out of these parameters, 426,839 are trainable, and 480 are non-trainable.
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Kumaria, A., Kulkarni, N., Jagtap, A. (2022). Product-Based Market Analysis Using Deep Learning. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_6
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DOI: https://doi.org/10.1007/978-981-16-2008-9_6
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