Sentiment Analysis: Predicting Product Reviews’ Ratings using Online Customer Reviews

2020 ◽  
Author(s):  
Ankit Taparia ◽  
Tanmay Bagla
Keyword(s):  
Sentiment Analysis ◽  
Product Reviews ◽  
Customer Reviews ◽  
Online Customer Reviews

Evaluating Annotated Dataset of Customer Reviews for Aspect Based Sentiment Analysis

2021 ◽  
Author(s):  
Dimple Chehal ◽  
Parul Gupta ◽  
Payal Gulati
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Product Reviews ◽  
K Nearest Neighbor ◽  
Customer Reviews ◽  
Percent Accuracy

Sentiment analysis of product reviews on e-commerce platforms aids in determining the preferences of customers. Aspect-based sentiment analysis (ABSA) assists in identifying the contributing aspects and their corresponding polarity, thereby allowing for a more detailed analysis of the customer’s inclination toward product aspects. This analysis helps in the transition from the traditional rating-based recommendation process to an improved aspect-based process. To automate ABSA, a labelled dataset is required to train a supervised machine learning model. As the availability of such dataset is limited due to the involvement of human efforts, an annotated dataset has been provided here for performing ABSA on customer reviews of mobile phones. The dataset comprising of product reviews of Apple-iPhone11 has been manually annotated with predefined aspect categories and aspect sentiments. The dataset’s accuracy has been validated using state-of-the-art machine learning techniques such as Naïve Bayes, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor and Multi Layer Perceptron, a sequential model built with Keras API. The MLP model built through Keras Sequential API for classifying review text into aspect categories produced the most accurate result with 67.45 percent accuracy. K- nearest neighbor performed the worst with only 49.92 percent accuracy. The Support Vector Machine had the highest accuracy for classifying review text into aspect sentiments with an accuracy of 79.46 percent. The model built with Keras API had the lowest 76.30 percent accuracy. The contribution is beneficial as a benchmark dataset for ABSA of mobile phone reviews.

Aspect-Based Sentiment Analysis of Online Product Reviews

2017 ◽  
pp. 175-191 ◽  
Author(s):  
Vinod Kumar Mishra ◽  
Himanshu Tiruwa
Keyword(s):  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Future Trend ◽  
Product Reviews ◽  
Customer Reviews ◽  
Part Of Speech ◽  
Lexical Approach ◽  
Sentiment Score ◽  
Pos Tagger

Sentiment analysis is a part of computational linguistics concerned with extracting sentiment and emotion from text. It is also considered as a task of natural language processing and data mining. Sentiment analysis mainly concentrate on identifying whether a given text is subjective or objective and if it is subjective, then whether it is negative, positive or neutral. This chapter provide an overview of aspect based sentiment analysis with current and future trend of research on aspect based sentiment analysis. This chapter also provide a aspect based sentiment analysis of online customer reviews of Nokia 6600. To perform aspect based classification we are using lexical approach on eclipse platform which classify the review as a positive, negative or neutral on the basis of features of product. The Sentiwordnet is used as a lexical resource to calculate the overall sentiment score of each sentence, pos tagger is used for part of speech tagging, frequency based method is used for extraction of the aspects/features and used negation handling for improving the accuracy of the system.

Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews

2012 ◽  
Author(s):  
Rahul Rai
Keyword(s):  
Text Mining ◽  
Design Process ◽  
Product Reviews ◽  
Product Attributes ◽  
New Approach ◽  
Preference Analysis ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Importance Ranking ◽  
The Individual

Identifying customer needs and preferences is one of the most important tasks in design process. Typically, a variation of interview based approaches is used to conduct need and preference analysis. In this paper, a new approach based on text mining online (internet based) customer reviews to supplement traditional methods of need and preference analysis is considered. The key idea underlying the proposed approach is to partition online customer generated product reviews into segments that evaluate the individual attributes of a product (e.g zoom capability and support of different image formats in a camcorder). Additionally, the proposed method also identifies the importance (ranking) that customers place on each product attributes. The method is demonstrated on 100 customer reviews submitted for camcorders on epinions.com over a two year period.

Research on Technology Oriented Framework of Aspects Extraction from Customer Reviews

2014 ◽  
Vol 488-489 ◽  
pp. 1358-1362
Author(s):  
Shi Li ◽  
Ming Yu Ji
Keyword(s):  
Sentiment Analysis ◽  
Product Information ◽  
Theoretical Framework ◽  
The Internet ◽  
Product Reviews ◽  
Valuable Data ◽  
Research Framework ◽  
Customer Reviews ◽  
Feature Mining ◽  
Oriented Research

As e-business develops rapidly, more and more product information and product reviews are posted on the Internet. These contents will have a great significance for companies and consumers. This paper focus on customer reviews of product, and construct a technology oriented research framework for the sentiment analysis. Further more an improved theoretical framework of aspects extraction is proposed, which based on products feature mining issues from customer reviews. This two theoretical framework can help researchers acquire supported valuable data for additional researches including the study of behavioral.

scholarly journals Examining Hidden Meaning of E-commerce Platform

2019 ◽  
Vol 8 (12) ◽  
pp. 257-261
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Food Products ◽  
Product Reviews ◽  
Specific Time ◽  
High Importance ◽  
Customer Reviews ◽  
Time Period ◽  
Emotion Lexicon ◽  
Product Rating

Different e-commerce companies try to maintain high importance for their customer satisfactions. It helps them to understand the performance of their products. Nowadays customers trust on the product reviews while shipping online. But it is a cumbersome task to handle millions of customer reviews within specific time period. Due to this problem consumers usually follow the set of reviews before taking decision for purchasing any products from online. Although, each consumer rates the product from 1 to 5 stars, these overall product rating judge products towards their customers satisfaction. Consumers also provide a text based summary as a review of their experiences and opinions about the products. Customer sentiment analysis is a method to process these customer reviews to summarize different products. In this manuscript, we analyzed the text summery of Amazon food products using NRC Emotion Lexicon to determine the overall responses of the products using eight emotions of the customers. Our result can be used to take further decision making for the future of the products.

A Data-Driven Approach to Product Usage Context Identification From Online Customer Reviews

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Dedy Suryadi ◽  
Harrison M. Kim
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Consumer Satisfaction ◽  
Data Driven ◽  
Data Sets ◽  
Product Usage ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Data Driven Approach ◽  
Usage Context

Abstract This paper proposes a data-driven methodology to automatically identify product usage contexts from online customer reviews. Product usage context is one of the factors that affect product design, consumer behavior, and consumer satisfaction. The previous works identify the usage contexts using the survey-based method or subjectively determine them. The proposed methodology, on the other hand, uses machine learning and Natural Language Processing tools to identify and cluster usage contexts from a large volume of customer reviews. Furthermore, aspect sentiment analysis is applied to capture the sentiment toward a particular usage context in a sentence. The methodology is implemented to two data sets of products, i.e., laptop and tablet. The result shows that the methodology is able to capture relevant product usage contexts and cluster bigrams that refer to similar usage context. The aspect sentiment analysis enables the observation of a product’s position with respect to its competitors for a particular usage context. For a product designer, the observation may indicate a requirement to improve the product. It may also indicate a possible market opportunity in a usage context in which most of the current products are perceived negatively by customers. Finally, it is shown that overall rating might not be a strong indicator for representing customer sentiment toward a particular usage context, due to the moderate linear correlation for most of the usage contexts in the case study.

Testing the SmartMunk's Story.ly App for the Analysis of Online Customer Reviews on Face Masks

2021 ◽  
pp. 90-116
Author(s):  
Arabela Briciu ◽  
Cristian-Laurențiu Roman ◽  
Victor-Alexandru Briciu
Keyword(s):  
Text Analysis ◽  
User Satisfaction ◽  
Product Reviews ◽  
Online Application ◽  
Customer Reviews ◽  
Customer Feedback ◽  
Specialized Software ◽  
Online Customer Reviews ◽  
Qualitative Part ◽  
Software Measures

This chapter aims to present the process of selecting and analyzing a number of reviews using a software solution (an online application) created specifically for text analysis and extracting user sentiment. This software measures the level of user satisfaction, analyzing product reviews and taking into account the qualitative part of the content generated by users. Analyzing online customer reviews with the help of specialized software can help both companies and other users. The software can also help us reach a conclusion regarding the analysis of reviews and customer feedback on products or services. This study can also be useful for customers or buyers who want to know the opinion of others about a product, having the opportunity to differentiate between positive and negative reviews.

A Unique Method of Constructing Brand Perceptual Maps by the Text Mining of Multimedia Consumer Reviews

2018 ◽  
Vol 9 (3) ◽  
pp. 1-22
Author(s):  
Amir Ekhlassi ◽  
Amirhosein Zahedi
Keyword(s):  
Opinion Mining ◽  
Product Reviews ◽  
Customer Reviews ◽  
Perceptual Maps ◽  
Online Customer Reviews ◽  
Visual Technique ◽  
Unique Method ◽  
Online Consumer ◽  
The Mind ◽  
Digital Tablets

Brand perceptual mapping is a visual technique, it displays how a brand is positioned in the mind of customers, as well as in relation to the competitors. With the rapid growth of e-commerce and the abundance of online consumer-generated content, there is no need for marketers to go through market research in order to understand consumers' opinions. Therefore, in this study, the authors propose a unique method which allows the building of a perceptual map automatically by mining consumer opinions from in particular online product reviews. The authors employ opinion mining techniques to extract and rank the product aspects that are important to customers, during purchasing digital tablets. Subsequently, they generate a score for each brand in these aspects and build the perceptual map using clustering of the brands by these scores. This proposed method is applied to the online customer reviews for digital tablets obtained from Amazon.com. The experimental results highlight the proposed technique is effective and able to correctly depict the position of a brand in its particular competitive environment.

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