Business Understanding

With a growing trend towards digitisation and prevalence of mobile phones and internet access, more consumers have an online presence and their opinions hold a good value for any product-based company, especially so for the B2C businesses. The industries are trying to fine-tune their strategies to suit the consumer needs, as the consumers leave some hints of their choices during their online presence.In this capstone project,solving a problem in the mobile phone industry of the US, one of the major smartphones markets in the world.

• By analysing the sentiment of the reviews, you can find the features of the phones that have resulted in positive/negative sentiments. This will help companies include or improve those particular features while developing a new product. If the data is of the competitor brands, the company will benefit by not repeating the same mistake during product development as their rival.
• Companies can effectively design their Ad campaign by highlighting the features that are most talked about among the consumers.
• Comparing the competitors' pricing and their market shares will help companies decide the price of their products.
• It can be assumed that if the number of reviews for a particular brand is high, the number of people buying phones of that brand is also high. This will help companies gauge the market share of their competitors.
• Before purchasing any product, we all look at similar products in various brands. This data will help the companies know their major competitors in the market.

Data Dictionary

1. Phone data as the .csv file: Contains the consumer activity information

overall: Overall rating for the particular product given by the user verified: Whether the user is a verified reviewer or not reviewerID: ID of the reviewer, e.g. A2SUAM1J3GNN3B asin: ID of the product, e.g. 0000013714. ASIN stands for Amazon Standard Identification Number. It is a 10-character alphanumeric unique identifier that is assigned by Amazon.com and its partners. It is primarily used for product-identification within their product catalogue style: Information about physical features like colour reviewerName: Name of the reviewer reviewText: Review provided by the reviewer summary: Summary of the review unixReviewTime: Time of the review (Unix time) vote: Upvotes or Downvotes of the review. A review with more upvotes can hold a higher significance image: Image of type .png or .jpg, etc provided by the reviewer review_sentiment: Pre-labelled sentiment of the review which can be either positive or negative. The labels will not be available for the real-time data, and a model needs to be built for such classification

2. Phone metadata as .json file: This data contains the product information and is independent of the consumer/reviewer activity and includes description, price, sales-rank, brand info, and co-purchasing links etc.

Category: List of categories the product belongs to, e.g., ['Cell Phones & Accessories', 'Cell Phones'] tech1: The specifications and technology of the product, e.g., Phablet ( a hybrid of phone and a tablet) tech2: Contains data similar to that of tech1 description: A short description of the product fit: A column which has no values for a mobile phone dataset title: Name of the product also_buy: Product IDs of the products that are generally bought along with the particular product image: Image of the product by the supplier brand: Brand of the product feature: Features of the product including specifications such as the 4G/3G, language, apps supported etc. rank: Ranking of the product as given by Amazon based on various parameters in different segments. It is a metric that explains the relationship among products within a category based on their sales performance. Sales rank is updated hourly, it can range from 1 to over 1 million, and is influenced by seasonality, and its algorithm remains unrevealed. also_view: Products which are also viewed while searching for this product. details: Contains details from a delivery point of view such as product dimension, shipping weight etc. main_cat: The main category to which the product belongs, e.g., “All Electronics” similar_item: Related products. price: Price of the product. asin: ID of the product, e.g., 0000031852

3. pos_words.txt : This is a text file that contains a corpus of positive words. This file can help in extracting some features out of the review text. There are multiple sources available online that gives you a corpus of positive negative words. we can also customise it based on your application.

4. neg_words.txt : Similar to the corpus of positive words, this text file contains a list of words that can be a part of the negative reviews of the users.

5. Stop_words_long.txt: Removing stopwords using the NLTK library where the drawback of it is, the negation words like nor, not, never are considered as stopwords.Remove those words may not affect a spam/ham classification.