The Internet has revolutionized the way we buy products. In the retail e-commerce world of online marketplace, where experiencing products are not feasible. Also, in today’s retail marketing world, there are so many new products are emerging every day. Therefore, customers need to rely largely on product reviews to make up their minds for better decision making on purchase. However, searching and comparing text reviews can be frustrating for users. Hence we need better numerical ratings system based on the reviews which will make customers purchase decision with ease.During their decision making process, consumers want to find useful reviews as quickly as possible using rating system. Therefore, models able to predict the user rating from the text review are critically important. Getting an overall sense of a textual review could in turn improve consumer experience. Also, it can help businesses to increase sales, and improve the product by understanding customer’s needs.The amazon review dataset for electronics products were considered. The reviews and ratings given by the user to different products as well as reviews about user’s experience with the product(s) were also considered.
The goal is to develop a model to predict user rating, usefulness of review and recommend most similar items to users based on collaborative filtering.
The electronics dataset consists of reviews and product information from amazon were collected. This dataset includes reviews (ratings, text, helpfulness votes) and product metadata (descriptions, category information, price, brand, and image features).Product Complete Reviews dataThis dataset includes electronics product reviews such as ratings, text, helpfulness votes. This dataset was obtained from http://jmcauley.ucsd.edu/data/amazon/. The original data was in json format. The json was imported and decoded to convert json format to csv format. The sample dataset is shown below:
Sample product reviews dataset
Each row corresponds to a customer review and includes the following variables:
Product MetadataThis dataset includes electronics product metadata such as descriptions, category information, price, brand, and image features. This dataset was obtained from http://jmcauley.ucsd.edu/data/amazon/. The json was imported and decoded to convert json format to csv format. The sample product meta dataset is shown below:
Sample product meta dataset
Each row corresponds to product and includes the following variables:
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Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?
We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.
But why do we need sentiment analysis?
Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.
It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.
Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.
Lastly, they are also used for spam filtering and removing unwanted content.
NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.
NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.
Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.
Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are
Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.
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In this tutorial, I will show you how to apply sentiment analysis to the text contained into a book through an Unsupervised Learning (UL) technique, based on the AFINN lexicon. This tutorial exploits the
afinn Python package, which is available only for English and Danish. If your text is written into a different language, you could translate it before in English and use the
This notebook applies sentiment analysis the Saint Augustine Confessions, which can be downloaded from the Gutemberg Project Page. The masterpiece is split in 13 books (or chapters). We have stored each book into a different file, named number.text (e.g. 1.txt and 2.txt). Each line of every file contains just one sentence.
You can download the code from my Github repository: https://github.com/alod83/papers/tree/master/aiucd2021
First of all import the
Afinn class from the
from afinn import Afinn
Then create a new
Afinn object, by specifying the used language.
afinn = Afinn(language=’en’)
afinn object contains a method, called
score(), which receives a sentence as input and returns a score as output. The score may be either positive, negative or neutral. We calculate the score of a book, simply by summing all the scores of all the sentence of that book. We define three variables> pos, neg and neutral, which store respectively the sum of all the positive, negative and neutral scores of all the sentences of a book.
Firstly, we define three indexes, which will be used after.
pos_index =  neg_index =  neutral_index = 
We open the file corresponding to each book through the
open() function, we read all the lines through the function
file.readlines() and for each line, we calculate the score.
Then, we can define three indexes to calculate the sentiment of a book: the positive sentiment index (pi), the negative sentiment index (ni) and the neutral sentiment index (nui). The pi of a book corresponds to the number of positive sentences in a book divided per the total number of sentences of the book. Similarly, we can calculate the ni and nui of a book.
for book in range(1,14): file = open('sources/' + str(book) + '.txt') lines = file.readlines() pos = 0 neg = 0 neutral = 0 for line in lines: score = int(afinn.score(line)) if score > 0: pos += 1 elif score < 0: neg += 1 else: neutral += 1 n = len(lines) pos_index.append(pos / n) neg_index.append(neg / n) neutral_index.append(neutral / n)
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