A Sentiment Analysis Based Approach for Understanding the User Satisfaction on Android Application

2020 ◽  
pp. 397-407
Author(s):  
Md. Mahfuzur Rahman ◽  
Sheikh Shah Mohammad Motiur Rahman ◽  
Shaikh Muhammad Allayear ◽  
Md. Fazlul Karim Patwary ◽  
Md. Tahsir Ahmed Munna
Keyword(s):  
Sentiment Analysis ◽  
User Satisfaction ◽  
Android Application

scholarly journals A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

2021 ◽  
Vol 22 (1) ◽  
pp. 53-66
Author(s):  
D. Anand Joseph Daniel ◽  
M. Janaki Meena
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
User Satisfaction ◽  
Performance Metrics ◽  
Computation Time ◽  
Feature Reduction ◽  
Training Data ◽  
Product Reviews ◽  
Online Product Reviews

Sentiment analysis of online product reviews has become a mainstream way for businesses on e-commerce platforms to promote their products and improve user satisfaction. Hence, it is necessary to construct an automatic sentiment analyser for automatic identification of sentiment polarity of the online product reviews. Traditional lexicon-based approaches used for sentiment analysis suffered from several accuracy issues while machine learning techniques require labelled training data. This paper introduces a hybrid sentiment analysis framework to bond the gap between both machine learning and lexicon-based approaches. A novel tunicate swarm algorithm (TSA) based feature reduction is integrated with the proposed hybrid method to solve the scalability issue that arises due to a large feature set. It reduces the feature set size to 43% without changing the accuracy (93%). Besides, it improves the scalability, reduces the computation time and enhances the overall performance of the proposed framework. From experimental analysis, it can be observed that TSA outperforms existing feature selection techniques such as particle swarm optimization and genetic algorithm. Moreover, the proposed approach is analysed with performance metrics such as recall, precision, F1-score, feature size and computation time.

Sentiment Analysis Technologies in AliMe — An Intelligent Assistant for E-commerce

2021 ◽  
pp. 2050016
Author(s):  
Shuangyong Song ◽  
Chao Wang ◽  
Siyang Liu ◽  
Haiqing Chen ◽  
Huan Chen ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Customer Service ◽  
User Satisfaction ◽  
Business Value ◽  
Analysis Framework ◽  
Artificial Intelligent ◽  
Single Task ◽  
Sentiment Detection ◽  
Intelligent Assistant ◽  
Key Techniques

In this paper, we introduce a sentiment analysis framework and its corresponding key techniques used in AliMe, an artificial intelligent (AI) assistant for e-commerce customer service, whose fundamental ability of sentiment analysis provides support for five upper-layer application modules: user sentiment detection, user sentiment comfort, sentimental generative chatting, user service quality control and user satisfaction prediction. Detailed implementation of each module is demonstrated and experiments show our framework not only performs well on each single task but also manifests its competitive business value as a whole.

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5666
Author(s):  
Cach N. Dang ◽  
María N. Moreno-García ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range ◽  
The One

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

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 Relationship between Result of Sentiment Analysis and User Satisfaction -The case of Korean Meteorological Administration-

2016 ◽  
Vol 16 (10) ◽  
pp. 393-402 ◽  
Author(s):  
In-Gyum Kim ◽  
Hye-Min Kim ◽  
Byunghwan Lim ◽  
Ki-Kwang Lee
Keyword(s):  
Sentiment Analysis ◽  
User Satisfaction

scholarly journals An Approach to Integrating Sentiment Analysis into Recommender Systems

2021 ◽  
Author(s):  
Cach Nhan Dang ◽  
María N. Moreno ◽  
Fernando De la Prieta
Keyword(s):  
Deep Learning ◽  
Collaborative Filtering ◽  
Sentiment Analysis ◽  
Recommender Systems ◽  
Recommender System ◽  
User Satisfaction ◽  
Online News ◽  
Learning Models ◽  
Adaptive Architecture ◽  
Wide Range

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

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