@inproceedings{244f2d7a895f48b995a51f607c48d21a,
title = "Customer Purchase Intention Prediction Using Text Analytical Models",
abstract = "Identifying the potential users matters a lot for the growth of any company. This can be estimated well in advance based on several factors like the user's opinion regarding a product before its sale is announced. Considering this, an application will be developed which will be easy to use, accurate intention prediction Website.The website is built using HTML, CSS, Javascript and Django. A text analytical model is built using an ensemble model, made by collecting tweets from users. The model will find out potential buyers who have tweeted about the product but are yet to purchase. The model would estimate the most likelihood of a customer's purchase. Various Parameters like Accuracy, Precision, Recall and F1 score are calculated for getting the best result. Presently there are not any notable applications available that use multiple algorithms and are used for purchase intention prediction with this sophisticated architecture, which makes the application unique.",
keywords = "BERT, Bi-LSTM, Machine Learning, Pur-chase Intention, SVM, Sentiment Analysis, Twitter mining",
author = "Meenal Shah and Skandan, \{K. A.\} and \{Shivani Sweta\}, S. and Garima Gaur and Prajwala, \{T. R.\}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 7th IEEE International conference for Convergence in Technology, I2CT 2022 ; Conference date: 07-04-2022 Through 09-04-2022",
year = "2022",
month = jan,
day = "1",
doi = "10.1109/I2CT54291.2022.9825140",
language = "English",
series = "2022 IEEE 7th International conference for Convergence in Technology, I2CT 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE 7th International conference for Convergence in Technology, I2CT 2022",
}