Abstract
The rapid shift towards digitalization today has actually transferred the market to an entirely digitalized platform. The participation of such a large number of users has given rise to a huge amount of data over the internet, proving the need for proper structuring and removal of unwanted and redundant data. The presence of a system that gives them the complete overview of a product is a dire need for the public today. Diving deep, we nd technologies that help us in the analysis and modification of data found over the internet. Sentiment analysis helps us nd the opinions people have towards a variety of entities, through a series of processes. Along with this, we have text summarisation which aids in the attainment of meaningful information from the wide range of irrelevant and redundant data found online. Clubbing these two, we can obtain concise reviews in addition to the overall sentiment towards selected entities. Here, we propose a model where we convolve into a system that provides the user with the overall recommendation found on popular e-commerce websites (Amazon, Flipkart and TripAdvisor). Starting with the collection of data from given sources, we pre-process the data, we combine machine learning with a lexicon-based approach, obtain the summaries and sentiments and eventually provide the user with the popular opinion behind the product.
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Kheruwala, H.A., Shah, J.V., Verma, J.P. (2020). Comparative Study of Sentiment Analysis and Text Summarization for Commercial Social Networks. In: Gupta, S., Sarvaiya, J. (eds) Emerging Technology Trends in Electronics, Communication and Networking. ET2ECN 2020. Communications in Computer and Information Science, vol 1214. Springer, Singapore. https://doi.org/10.1007/978-981-15-7219-7_18
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DOI: https://doi.org/10.1007/978-981-15-7219-7_18
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