scholarly journals Business intelligence analytics using sentiment analysis-a survey

2019 ◽  
Vol 9 (1) ◽  
pp. 613
Author(s):  
Prakash P. Rokade ◽  
Aruna Kumari D
Keyword(s):  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Business Intelligence ◽  
Text Analysis ◽  
Analysis Techniques ◽  
The People ◽  
Suitable Technique ◽  
Extract Information

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.

scholarly journals Evaluation of Sentiment Data using Classifier Model in Rapid Miner Tool

2019 ◽  
Vol 9 (1) ◽  
pp. 2966-2972
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Analysis ◽  
Group Communication ◽  
Analysis Techniques ◽  
Online Portal ◽  
Almost All ◽  
Extract Information

Evaluation of internet and the usage of internet as websites which is for penetrating to gain a specific requirements, like group communication as social networks (such as face book, twitter,etc.,) ,blogs for opinions, online portals (such as iGoogle, MSN) for communication, experience as reviews, suggestions as opinions, combination of reviews and opinions as recommendations, ratings and feedbacks which is identified and elevating in almost all the field now-a-days. The writers of online portal, review, opinion and recommendation in any social media take measures as beneficial factor for the improvement of businesses, organization, governments and mostly individuals. When this content boost up the study of content and the need of data mining, text mining techniques and sentiment analysis is inescapable. Natural language processing and text analysis techniques are used in sentiment analysis to recognize and extract information from the text [1]. This paper provides a result of sentiment analysis with the intellectual tool named Rapid Miner to show the sentiment comments about the contents in the online traders.

Tracking How a Change in a Telecom Service Affects Its Customers Using Sentiment Analysis and Personality Insight

2020 ◽  
Vol 11 (3) ◽  
pp. 33-46
Author(s):  
Ammar Adl ◽  
Abdelsadeq Khamis Elfergany
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Customer Retention ◽  
Full Description ◽  
Important Process ◽  
Learning Stage ◽  
Analysis Techniques ◽  
Telecom Service

Tracking the effect of change a telecom service on customer feeling is an important process for telecom companies. As a result of tangible growth and large competition among telecom companies, customer retention and satisfaction are the most important challenges faced by telecom companies nowadays. Customer retention can be achieved by identifying the feeling of the telecom customers after changing service and take care of the customers by modifying the services that aren't accepted by its customers. Hence, this article was done by using a combination of four stages of: text pre-processing, personality analysis, sentiment analysis, and a chatbot system. This article shows the effect of using the personality traits, agreeableness and emotional range, with sentiment analysis to help reaching a full description of customer feel. Combining the sentiment analysis Naïve Bayes technique in the natural language processing and personality insights pre-learning stage and adding feedback using the obtained results achieves higher accuracy than using the traditional sentiment analysis techniques.

Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

2020 ◽  
pp. 939-956
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

scholarly journals A Culling System for A Recipe using Sentiment Analysis Techniques

2019 ◽  
Vol 8 (5S3) ◽  
pp. 123-127
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Visual Recognition ◽  
Text To Speech ◽  
Web Crawler ◽  
Analysis Techniques ◽  
Bayes Algorithm ◽  
Different Sources

This System helps the user for providing the recipes in which they are particularly interested in a mentioned criteria of ingredients on which the user wants to prepare a recipe. The input given by the user like the ingredient on which he/she wants to prepare a recipe can be of text, speech or visual. The process of providing a list of gathered recipes from different trusted and verified sources is performed with the help of Sentiment Analysis(SA), Watson Text-to-Speech and Watson Visual Recognition(WVR). The recipe extraction from different sources which are required for the user is retrieved with the help of a standard Web Crawler. The tools and technologies used for the proposed system are from Artificial Intelligence(AI), Natural Language Processing(NLP). The system proposed assists the user in providing a list of recipes in a prioritized order based on the optimization process performed by the Naïve Bayes Algorithm(NBA) of Sentiment Analysis. In addition, the displayed results of recipes have been reviewed and rated by different users from different sources.

scholarly journals A Security-Related Reputation Scheme of Android Apps Based on NLP Analysis of Comments

2020 ◽  
Author(s):  
Franklin Tchakounté ◽  
Athanase Esdras Yera Pagore ◽  
Marcellin Atemkeng ◽  
Jean Claude Kamgang
Keyword(s):  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Text Analysis ◽  
Positive Functional ◽  
Fine Grained ◽  
Android Apps ◽  
Depth Analysis ◽  
Android Applications ◽  
Google Play

Comments are exploited by product vendors to measure satisfaction of consumers. With the advent of Natural Language Processing (NLP), comments on Google Play can be processed to extract knowledge on applications such as their reputation. Proposals in that direction are either informal or interested merely on functionality. Unlike, this work aims to determine reputation of Android applications in terms of confidentiality, integrity, availability and authentication (CIAA). This work proposes a model of assessing app reputation relying on sentiment analysis and text analysis of comments. While assuming that comments are reliable, we collect Google Play applications subject to comments which include security keywords. An in-depth analysis of keywords based on Naive Bayes classification is made to provide polarity of any comment. Based on comment polarity, reputation is evaluated for the whole application. Experiments made on real applications including dozens to billions of comments, reveal that developers lack to make efforts to guarantee CIAA services. A fine-grained analysis shows that not security reputed applications can be reputed in specific CIAA services. Results also show that applications with negative security polarities display in general positive functional polarities. This result suggests that security checking should include careful comment analysis to improve security of applications.

scholarly journals Deep learning approach to text analysis for human emotion detection from big data

2022 ◽  
Vol 31 (1) ◽  
pp. 113-126
Author(s):  
Jia Guo
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Analysis ◽  
Question Answering ◽  
Word Embeddings ◽  
Emotion Detection ◽  
Human Emotion

Abstract Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing (NLP) and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis (DLSTA) has been proposed for human emotion detection using big data. Emotion detection from textual sources can be done utilizing notions of Natural Language Processing. Word embeddings are extensively utilized for several NLP tasks, like machine translation, sentiment analysis, and question answering. NLP techniques improve the performance of learning-based methods by incorporating the semantic and syntactic features of the text. The numerical outcomes demonstrate that the suggested method achieves an expressively superior quality of human emotion detection rate of 97.22% and the classification accuracy rate of 98.02% with different state-of-the-art methods and can be enhanced by other emotional word embeddings.

Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

2020 ◽  
pp. 879-896
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

Organizational Online Reputation Measurement Through Natural Language Processing and Sentiment Analysis Techniques

2021 ◽  
pp. 60-71
Author(s):  
Christian Orrego ◽  
Luisa Fernanda Villa ◽  
Lina Maria Sepúlveda-Cano ◽  
Lillyana M. Giraldo M.
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Analysis Techniques ◽  
Reputation Measurement ◽  
Online Reputation
Sign in / Sign up

Export Citation Format

Share Document