CUSENT

2010 ◽  
pp. 89-101 ◽  
Author(s):  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
Myriam Mencke ◽  
Juan M. Gómez-Berbís
Keyword(s):  
Information Systems ◽  
Web 2.0 ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Research Work ◽  
The Novel ◽  
Customer Feedback ◽  
Commercial Use ◽  
Corporate Blogs ◽  
Innovation Processes

The current chapter introduces CUSENT, a tool for semantics-enhanced sentiment analysis of customer opinions expressed in corporate blogs. The research work presents the examination of emotions and sentiments from the perspective of information systems, and, in particular, provides a review of the principal efforts for the conceptualization of emotions and sentiments in texts. Subsequently, a description of the proposed architecture of the platform is outlined. The authors aim to contribute a solution which automates the analysis of customer opinions in company blogs that relies on existing techniques, but further exploits these methods to store and reuse customer feedback. The novel combination of opinion mining with an ontology of emotions can thus be used in organizational creation and innovation processes, which characterize the new forms of communication derived from the institutional and commercial use of Web 2.0.

Threat Modeling

2010 ◽  
pp. 228-246
Author(s):  
Nishtha Srivastava ◽  
Sumeet Gupta ◽  
Mayank Mathur
Keyword(s):  
Web 2.0 ◽  
Research Work ◽  
Complete Analysis ◽  
New Approach ◽  
Threat Modeling ◽  
Commercial Use ◽  
Modeling Approaches ◽  
Modeling Approach ◽  
Public Networks ◽  
Informal Method

This research work proposes a threat modeling approach for Web 2.0 applications. The authors’ approach is based on applying informal method of threat modeling for Web 2.0 applications. Traditional enterprises are skeptical in adopting Web 2.0 applications for internal and commercial use in public-facing situations, with customers and partners. One of the prime concerns for this is lack of security over public networks. Threat modeling is a technique for complete analysis and review of security aspects of application. The authors will show why existing threat modeling approaches cannot be applied to Web 2.0 applications, and how their new approach is a simple way of applying threat modeling to Web 2.0 applications.

scholarly journals Robustious Feature Selection Based Genetic Algorithm (RFS-GA) For Cross Domain Opinion Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 6267-6279
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Performance Metrics ◽  
Research Work ◽  
Features Selection ◽  
Cross Domain ◽  
Multiple Challenges ◽  
Day By Day

Day by day the requirement of information for processing the sentiment analysis is getting increased multiple times. For these kind of reasons, feature selection is utilized to detect the opinion among different reviews and comments. Sentiment analysis is becoming like phenomenon due to increase of social media’s popularity. Currently, significant advancements are shown in this research domain, but still multiple challenges are to be solved – i.e., sentiment analysis in cross domains. In this paper rumbustious feature selection based genetic algorithm is proposed to address the problem of analyzing the sentiments in cross domain. It performs classification based optimistic-class and pessimistic-class. The dataset used to this research work includes books, DVDs, gadgets and kitchen appliances. Initially the features selection is performed and opinion mining is performed by Genetic Algorithm. Benchmark performance metrics are selected for measuring the performance of proposed work against existing method. Results depict that the proposed work has better performance than that of the existing work as far as chosen performance metrics.

scholarly journals A Inspection on Sentiment Research of Big Data: Techniques, Open Challenges

2019 ◽  
Vol 8 (6S4) ◽  
pp. 362-365
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Service Industry ◽  
Research Work ◽  
Educational Institutions ◽  
Business Organizations ◽  
The Public ◽  
Day By Day

Due to the invention of Web 2.0, the users have become more interest to share their content day by day. The emergence of various social networking sites has added to a greater extent to these activities. These provide a very good platform for the users to share the opinions of the persons across the globe. The opinions shared by the customers on the web can have the prevalent over the service industry. Many industries such as educational institutions, researchers, business organizations are concentrating opinion mining which is also called as sentiment analysis (SA) to retrieve the views and opinions posted by the public. This paper made a survey on Sentiment Analysis (SA) which aims to discusses technical aspects of SA (techniques, types) .This paper further highlights the main challenges faced by SA. These challenges present a lot of scope for research work in the future

scholarly journals Sentiment Analysis Using Machine Learning Approaches (Lexicon based on movie review dataset)

2020 ◽  
Vol 2 (3) ◽  
pp. 145-152
Author(s):  
Ayushi Mitra
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Research Work ◽  
Emotional States ◽  
Learning Approaches ◽  
Data Set

Sentiment analysis or Opinion Mining or Emotion Artificial Intelligence is an on-going field which refers to the use of Natural Language Processing, analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic Natural Language Processing techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.

scholarly journals Twitter Sentiment Analysis on Government Law Using Real Time Data

2021 ◽  
pp. 200-205
Author(s):  
Sujata Patil ◽  
Bhavesh Wagh ◽  
Aditya Bhinge ◽  
Aakash Sahal ◽  
Prof. Madhav Ingale
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Opinion Mining ◽  
Central Government ◽  
Research Work ◽  
Research Area ◽  
User Generated Content ◽  
Time Data ◽  
Social Data

Social media monitoring has been growing day by day so analyzing social data plays an important role in knowing people's behavior. So we are analyzing Social data such as Twitter Tweets using sentiment analysis which checks the opinion of people related to government schemes that are announced by the Central Government. This paper-based is on social media Twitter datasets of particular schemes and their polarity of sentiments. The popularity of the Internet has been rapidly increased. Sentiment analysis and opinion mining is the field of study that analyses people's opinions, sentiments, evaluations, attitudes, and emotions from written language. User-generated content is highly generated by users. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. It is difficult to analyze or summarize user-generated content. Most of the users write their opinions, thoughts on blogs, social media sites, E-commerce sites, etc. So these contents are very important for individuals, industry, government, and research work to make decisions. This Sentiment analysis and opinion mining research is a hot research area that comes under Natural Language processing. We plot and calculate numbers of positive, negative, and neutral tweets from each event.

Optimization Assisted Convolutional Neural Network for Sentiment Analysis with Weighted Holoentropy-based Features

2021 ◽  
pp. 1-37
Author(s):  
Hema Krishnan ◽  
M. Sudheep Elayidom ◽  
T. Santhanakrishnan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Opinion Mining ◽  
Research Work ◽  
Constant Term ◽  
Batch Size ◽  
Major Work

Analyzing and gathering the people’s reactions on product trading, public services, etc. are crucial. Sentiment analysis (also termed as opinion mining) is a usual dialogue preparing act that plans on discovering the sentiments after opinions in texts on changing subjects. This research work adopts a novel sentiment analysis approach that comprises six phases like (i) Pre-processing, (ii) Keyword extraction and its sentiment categorization, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Accordingly, the Mongodb documented tweets initially underwent pre-processing with stop word removal, stemming, and blank space removal. Regarding the extracted keywords, the existing semantic words are derived after categorizing the sentiment of keywords. Additionally, the semantic similarity score is evaluated along with their keywords. The subsequent step is feature extraction, where the Holoentropy features such as cross Holoentropy and joint Holoentropy are formulated. Along with this, the extraction of weighted holoentropy features is the major work, where weight is multiplied with the holoentropy features. Moreover, in order to enhance the performance of classification results, the constant term utilized in evaluating the weight function is optimized. For this optimal tuning, a new, improved algorithm termed as Self Adaptive Moth Flame Optimization (SA-MFO) is introduced, which is the adaptive version of MFO algorithm. For classification, this paper aims to use the Deep Convolutional Neural network (DCNN), where the batch size is fine-tuned using the same SA-MFO algorithm. Finally, the performance of the proposed work is compared over other conventional models with respect to different performance measures.

scholarly journals Insights to Problems, Research Trend and Progress in Techniques of Sentiment Analysis

2017 ◽  
Vol 7 (5) ◽  
pp. 2818 ◽  
Author(s):  
Kumar P. K. ◽  
Nandagopalan S.
Keyword(s):  
Sentiment Analysis ◽  
Opinion Mining ◽  
Research Work ◽  
Research Trend ◽  
The Political ◽  
Important Research ◽  
Research Implementation ◽  
Pervasive Environment ◽  
Research Gap ◽  
Open Issues

The research-based implementations towards Sentiment analyses are about a decade old and have introduced many significant algorithms, techniques, and framework towards enhancing its performance. The applicability of sentiment analysis towards business and the political survey is quite immense. However, we strongly feel that existing progress in research towards Sentiment Analysis is not at par with the demand of massively increasing dynamic data over the pervasive environment. The degree of problems associated with opinion mining over such forms of data has been less addressed, and still, it leaves the certain major scope of research. This paper will brief about existing research trends, some important research implementation in recent times, and exploring some major open issues about sentiment analysis. We believe that this manuscript will give a progress report with the snapshot of effectiveness borne by the research techniques towards sentiment analysis to further assist the upcoming researcher to identify and pave their research work in a perfect direction towards considering research gap.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

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