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@article{151364,
        author = {G. Sumanth Kumar and G. Sai Kiran and V. Shiva Kumar and N. Sudheer and Dr. Suwarna Gothane},
        title = {PRODUCT REVIEW SENTIMENT  ANALYSIS USING WEAKLY SUPERVISED DEEP EMBEDDING},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {12},
        pages = {537-540},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=151364},
        abstract = {Now-a-days product reviews are becoming more valuable for the upcoming buyers to help them to make their right decisions to take the products in E-commerce websites. Recently, the Deep learning playing a major role in solving the problem related to sentiment classification. A neural network will be useful to represent automatically without human efforts. However, deep learning play’s role on the highly relies that available in the large-scale training data set.  We have implemented a novel deep learning framework for the product review analysis. With the help of wireless technology, the internet becomes a valuable place for online learning, exchanging ideas, reviews for a product or service. Reviews in the internet could be in millions for a product or services which make it difficult to track and understand customer opinions. Sentiment analysis is an emerging area of research to extract the subjective information in source materials. In this paper, we have demonstrated a novel deep learning framework for the product review analysis.},
        keywords = {Sentiment Analysis, Naive Bayes classifiers, Conventional Neural Networks.},
        month = {},
        }