Context-Based Sentiment Analysis on Amazon Product Customer Feedback Data

2020 ◽  
pp. 515-527
Author(s):  
C. Sindhu ◽  
Dewang Rajkakati ◽  
Chinmay Shelukar
Keyword(s):  
Sentiment Analysis ◽  
Customer Feedback ◽  
Feedback Data

Sentiment Analysis of Customer Feedback on Restaurant Reviews

2019 ◽  
Author(s):  
Spoorthi C ◽  
Dr. Pushpa Ravikumar ◽  
Mr. Adarsh M.J
Keyword(s):  
Sentiment Analysis ◽  
Customer Feedback ◽  
Restaurant Reviews

A Novel Algorithm for Sentiment Analysis of Online Movie Reviews

2018 ◽  
pp. 106-140
Author(s):  
Bisma Shah ◽  
Farheen Siddiqui
Keyword(s):  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Learning Approaches ◽  
World Knowledge ◽  
K Nearest Neighbor ◽  
Customer Feedback ◽  
Novel Approach ◽  
The One ◽  
Novel Algorithm

Others' opinions can be decisive while choosing among various options, especially when those choices involve worthy resources like spending time and money buying products or services. Customers relying on their peers' past reviews on e-commerce websites or social media have drawn a considerable interest to sentiment analysis due to realization of its commercial and business benefits. Sentiment analysis can be exercised on movie reviews, blogs, customer feedback, etc. This chapter presents a novel approach to perform sentiment analysis of movie reviews given by users on different websites. Also, challenges like presence of thwarted words, world knowledge, and subjectivity detection in sentiments are addressed in this chapter. The results are validated by using two supervised machine learning approaches, k-nearest neighbor and naive Bayes, both on method of sentiment analysis without addressing aforementioned challenges and on proposed method of sentiment analysis with all challenges addressed. Empirical results show that proposed method outperformed the one that left challenges unaddressed.

scholarly journals Visual opinion analysis of customer feedback data

2009 ◽  
Author(s):  
Daniela Oelke ◽  
Ming Hao ◽  
Christian Rohrdantz ◽  
Daniel A. Keim ◽  
Umeshwar Dayal ◽  
...  
Keyword(s):  
Customer Feedback ◽  
Opinion Analysis ◽  
Feedback Data

An Advance Approach toward Sentiment Analysis using Swarm Intelligence

2021 ◽  
Vol 9 (VI) ◽  
pp. 1710-1713
Author(s):  
Miss. Riddhi Mandal
Keyword(s):  
Artificial Intelligence ◽  
Social Media ◽  
Sentiment Analysis ◽  
Swarm Intelligence ◽  
Crucial Role ◽  
Complex Problem ◽  
Analysis Approach ◽  
Customer Feedback ◽  
The World

Modernization is the key feature for the development of Society. With the timespan people are making growth with trends in technology. Around the decades, there were many technologies which have been stepped up over the industry and made the transformation in the society and have made tremendous development throughout the world. Similarly, In the 21st decades Social media (like Facebook, Twitter, what’s app, Instagram & many more) have become one of the emphasized network mediums. Millions of people are using social media to get in touch with people staying far away from them. There are millions of data over it which is non-hierarchical and need to store and use it for feedback and other usage. Not only in Social Media, in the business & marketing sector too, customer feedback plays a crucial role. For maintaining and segregating data in a systematic way, sentiment analysis is being used which makes the task easier and helps to understand the data in a better way. In this paper, we are presenting a sentiment analysis approach using Swarm Intelligence, which could be more beneficial in such tasks to solve the complex problem. The concept is correlated with technology Artificial Intelligence.

Integrating sentiment analysis and term associations with geo-temporal visualizations on customer feedback streams

2012 ◽  
Author(s):  
Ming Hao ◽  
Christian Rohrdantz ◽  
Halldór Janetzko ◽  
Daniel Keim ◽  
Umeshwar Dayal ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Customer Feedback

scholarly journals Sentiment Analysis of Movies on Social Media using R Studio

2019 ◽  
Vol 8 (6) ◽  
pp. 2171-2175
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Media Analysis ◽  
Literature Study ◽  
Social Media Analysis ◽  
Customer Feedback ◽  
Twitter Data ◽  
User Data ◽  
Market Trends ◽  
Twitter Data Analysis

This paper presents sentiment analysis of twitter data on movies using R-studio. Twitter is one of the largest social media that shares user opinion about a thing or event that happens all around the world. Recently social media analysis gained importance in digital marketing. User tweets about a product or event, person, movie, etc., are analyzed to know market trends and customer feedback. In this paper, first we have performed literature study on various methods used in twitter data analysis. Second, we have discussed about the steps involved in accessing twitter data. Finally, we have performed sentiment analysis on tweeter data for the movies titled kabali, Bharath Ane Nenu Mersal, and Dangal. User data for the movies are classified into positive, neutral and negative based on DBM and SVM. Sentiment scores are used as evaluation metrics. Results shows DBM is effective in classifying sentiments and produced better sentiment scores compared to SVM. Results are helpful in identifying popularity of the movies and audience feedback about the movies.

scholarly journals Sentiment analysis of unstructured customer feedback for a retail bank

ORiON ◽  
2020 ◽  
Vol 36 (1) ◽  
Author(s):  
J Kazmaier ◽  
JH van Vuuren
Keyword(s):  
Sentiment Analysis ◽  
Customer Feedback

scholarly journals Sentiment Analysis of Android Application Feedback Data using Hadoop

IJARCCE ◽  
2016 ◽  
Vol 5 (12) ◽  
pp. 29-35
Author(s):  
Prakash R. Andhale ◽  
Prof. Rokade S.M.
Keyword(s):  
Sentiment Analysis ◽  
Android Application ◽  
Feedback Data

scholarly journals Impact of Customer Feedback System using Machine Learning Algorithms for Sentiment Mining

2020 ◽  
Vol 9 (4) ◽  
pp. 1475-1483
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Product Reviews ◽  
Machine Learning Classification ◽  
Customer Feedback ◽  
Specificity And Sensitivity ◽  
Sentiment Mining

The process of discovering and analyzing the customer feedback using Natural Language Processing (NLP) is said to be sentiment analysis. Based on the surge over the concept of rating level in sentiment analysis, sentiment is utilized as an attribute for certain aspects or features that get expressed and more attention are provided to the problem of detecting the customer reviews. Despite the wide use and popularity of some methods, a better technique for identifying the polarity of a text data is hard to find. Machine learning has recently attracted attention as an approach for sentiment analysis. This work extends the idea of evaluating the performance of various Machine Learning (ML) classifiers namely logistic regression, Naive Bayes, Support Vector Machine (SVM) and Neural Network (NN).To show their effectiveness in sentiment mining of customer product reviews, the customer feedback has been collected from Grocery and Gourmet Food. Nearly 90 thousands customers feedback reviews of various product related categories namely Product ID, rating, review test, review time reviewer ID and reviewer name are used in this analysis. The performance of the classifiers is measured in terms of accuracy, specificity and sensitivity. From the experimental results, the better machine learning classification algorithm is proposed for sentiment mining using online shopping customer review data.

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