E-commerce is the way to go, and the last decade has seen a dramatic surge in the number of e-commerce platforms. Everything, from food to clothing, is purchased on the internet. E-commerce portals have acquired most of the market from brick-and-mortar retailers for electronic boxed products such as mobile phones, laptops, and televisions, where there is minimal opportunity for scrutinising the product before purchasing (Bakos, 2001). Prospective clients have therefore learned to rely on the reviews left by other customers and evaluate the characteristics and costs of various electronic devices sold on various e-commerce sites such as Best Buy, Walmart, and Amazon. Social media apps such as Twitter are also venues where one may look for product reviews before purchasing them to make informed judgments (Kalia et. al., 2018).
Consequently, customer reviews play a significant part in enabling customers to understand what they buy and make informed purchases. For example, if someone wants to buy a phone, he or she will typically seek up reviews for that phone on a variety of e-commerce portals. Furthermore, various clients will have differing perspectives on the phone. It is impossible to keep track of all these factors. This will not only take a long time, but it will also be incredibly complicated.
Because of technological advancements, it is now possible to bring the answers to all these questions in one place and compare or understand the product much more readily than previously, making it easier to understand a certain product. This can be accomplished by developing a website that contains and analyses all recent reviews, including both good and negative reviews of the product that a client plans to buy.
The goal of this project is to create an application that can retrieve and analyse product reviews (mobile phones) from numerous retail websites and show the information in a single platform, enabling a buyer to make an informed purchase decision.
The initiative will attempt to answer problems such as:
• How do different customers give feedback on certain products (mobile phones) and how do one acquire client sentiments on the mobile phone product?
Keeping these questions and the overarching goal in mind, I defined specific goals for the task. The goals are as follows:
1. Creating an app that can provide the following information for the products searched for:
· Display polarity (positive, negative, or neutral), i.e., the product's sentiment score
· Display the product's most recent ten reviews or testimonials.
· A chart displaying its average sentiment score over the previous six months
2. Writing programmes in Jupyter notebook (python) to retrieve consumer reviews from four websites: BestBuy, Walmart, Amazon, and Twitter. These are all existing reviews, and no new reviews will be collected through separate questionnaires.
3. Using Python to clean and analyse data collected for text analytics and sentiment analysis to gain relevant insights from feedback data.
4. Finally, the user interfaces for the two applications must be created (rendered). Django will be used for this, and HTML codes will be used.
When it comes to designing applications and web development, Django and Flask are both excellent choices to consider (Ghimire, 2020). Flask and Django are both Python-based frameworks, although the difference between them is that Flask is designed for micro applications and rapid development, whilst Django is designed for simple and straightforward projects. As a result, I made the decision to use Django instead of Flask, keeping the restrictions and goal in mind.
1.2 Problem Statement
As previously said, it is a challenge for prospective customers to gather information on a given product from a variety of various websites. With technology advancement, it is now feasible to gather all the answers in one location and compare or comprehend the product much more readily than it was before possible, saving time and money. Creating a website that provides all the most current product reviews, whether they are favourable, neutral, or negative in nature, for the product the client wishes to purchase is one method of achieving this goal.
Truly speaking, such an application is not now accessible elsewhere, and if it can be developed utilising data science technologies, it will be of tremendous assistance to potential clients in their selection of the appropriate product among the various possibilities. Customers will benefit from being able to obtain big amounts of data in a short period of time thanks to this feature. It will also analyse the reviews and provide it with a sentiment score, which represents the consumers' perspective of the product. Making a tool that allows customers to evaluate a product in a short amount of time would be extremely beneficial to them in the long run and will enable them to make educated decisions before purchasing the items.
1.3 Research Questions
The tool that will be created as part of this project will attempt to answer queries such as:
• How should the necessary information, i.e., reviews, be collected
• From whom are these reviews going to be sourced?
• How will the data be assessed, given that it is entirely composed of language rather than numbers?
• What is the best way to understand the sentiment of the review?
• How is the interface going to be designed?
• What will the user interface look like? It must be simple to read for the benefit of the customers.
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