scholarly journals AUTOMATED SENTIMENT ANALYSIS AN AUTOMATED ANALYSIS OF NEWS FEEDS

2013 ◽  
pp. 60-62
Author(s):  
KARTHIK BALASUBRAMANIAN ◽  
APARAJITH CHANDRAN
Keyword(s):  
Sentiment Analysis ◽  
Automated Analysis ◽  
Unstructured Data ◽  
The Internet ◽  
Accurate Analysis ◽  
Efficient System ◽  
Business Information

This Paper explains the importance of Sentiment Analysis in today's business. Information which is hidden as unstructured data in the Internet can be utilized more efficiently. In this paper we quote an approach which explains the experiment for collection of news data and analyzing the sentiments for those data. The results provide almost accurate analysis outcomes with a few discrepancies. These are also explained and research is in progress towards making an efficient system.

A Survey on Database in Cloud Computing with Reference to the Traditional Database

2017 ◽  
Vol 7 (11) ◽  
pp. 150
Author(s):  
Eter Basar ◽  
Ankur Pan Saikia ◽  
L. P. Saikia
Keyword(s):  
Information Technology ◽  
Cloud Computing ◽  
The Internet ◽  
Data Organization ◽  
Technology Industry ◽  
Business Applications ◽  
Business Information ◽  
Points Of Interest ◽  
Cloud Databases ◽  
Online Reservation

Data Technology industry has been utilizing the customary social databases for around 40 years. Be that as it may, in the latest years, there was a generous transformation in the IT business as far as business applications. Remain solitary applications have been supplanted with electronic applications, conferred servers with different proper servers and committed stockpiling with framework stockpiling. Lower expense, adaptability, the model of pay-as-you-go are the fundamental reasons, which caused the conveyed processing are transformed into reality. This is a standout amongst the hugest upsets in Information Technology, after the development of the Internet. Cloud databases, Big Table, Sherpa, and SimpleDB are getting the opportunity to be more natural to groups. They featured the hindrances of current social databases as far as convenience, adaptability, and provisioning. Cloud databases are basically utilized for data raised applications, for example, stockpiling and mining of gigantic information or business information. These applications are adaptable and multipurpose in nature. Various esteem based data organization applications, such as managing an account, online reservation, e-exchange and stock organization, and so on are delivered. Databases with the help of these sorts of uses need to incorporate four essential highlights: Atomicity, Consistency, Isolation, and Durability (ACID), in spite of the fact that utilizing these databases isn't basic for utilizing as a part of the cloud. The objective of this paper is to discover the points of interest and disservices of databases generally utilized in cloud frameworks and to survey the difficulties in creating cloud databases

Data Mining Techniques and Applications

2018 ◽  
Vol 9 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Nayem Rahman
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Unstructured Data ◽  
The Internet ◽  
Holistic View ◽  
Data Mining Techniques ◽  
Extant Literature ◽  
Business Environments ◽  
Data Volume ◽  
Complete Set

Data mining has been gaining attention with the complex business environments, as a rapid increase of data volume and the ubiquitous nature of data in this age of the internet and social media. Organizations are interested in making informed decisions with a complete set of data including structured and unstructured data that originate both internally and externally. Different data mining techniques have evolved over the last two decades. To solve a wide variety of business problems, different data mining techniques are developed. Practitioners and researchers in industry and academia continuously develop and experiment varieties of data mining techniques. This article provides an overview of data mining techniques that are widely used in different fields to discover knowledge and solve business problems. This article provides an update on data mining techniques based on extant literature as of 2018. That might help practitioners and researchers to have a holistic view of data mining techniques.

scholarly journals Amalgamation of Clustering and Meta-heuristic Optimization Techniques for Automated MR Brain Analysis

2019 ◽  
Vol 9 (2S2) ◽  
pp. 642-647
Keyword(s):  
Computation Time ◽  
Medical Practitioner ◽  
Automated Analysis ◽  
Disease Diagnosis ◽  
Tumor Location ◽  
Optimization Techniques ◽  
Heuristic Optimization ◽  
Computer Assisted ◽  
Accurate Analysis ◽  
Tumor Region

Interest in computer-assisted image analysis in increasing among the radiologist as it provides them the additional information to take decision and also for better disease diagnosis. Traditionally, MR image is manually examined by medical practitioner through naked eye for the detection and diagnosis of tumor location, size, and intensity; these are difficult and not sufficient for accurate analysis and treatment. For this purpose, there is need for additional automated analysis system for accurate detection of normal and abnormal tumor region. This paper introduces the new semi-automated image processing method to identify the brain tumor region in Magnetic Resonance Image (MRI) using c means clustering technique along with meta-heuristic optimization, based on Jaya optimization algorithm. The resultant performance of the proposed algorithm (FCM +JA) is examined with the help of key analyzing parameters, MSE-Mean Square Error, PSNR-Peak Signal to Noise Ratio, DOI-Dice Overlap Index and CPU memory utilization. The experimental results of this method show better and enhanced tumor region display in reduced computation time.

scholarly journals Study of Automotive Brands Popularity in Indonesia Using Twitter Data

2016 ◽  
Vol 3 (1) ◽  
pp. 23-33
Author(s):  
Stevent Efendi ◽  
Alva Erwin ◽  
Kho I Eng
Keyword(s):  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Automotive Industry ◽  
Real World ◽  
The Internet ◽  
Brand Preference ◽  
Twitter Data ◽  
Wide Range ◽  
Widespread Phenomenon

Social media has been a widespread phenomenon in the recent years. People shared a lot of thought in social media, and these data posted on the internet could be used for study and researches. As one of the fastest growing social network, Twitter is a particularly popular social media to be studied because it allows researchers to access their data. This research will look the correlation between Twitter chatter of a brand and the sales of brands in Indonesia. Factors such as sentiment and tweet rate are expected to be able to predict the popularity of a brand. Being one of the biggest industries in Indonesia, automotive industry is an interesting subject to study. A wide range of people buys vehicles, and even gather as communities based on their car or motorcycle brand preference. The Twitter results of sentiment analysis and tweet rate will be compared with real world sales results published by GAIKINDO and AISI.

scholarly journals Rule Based Morphological Variation Removable Stemming Algorithm

2019 ◽  
Vol 8 (4) ◽  
pp. 1809-1814
Keyword(s):  
Feature Selection ◽  
Sentiment Analysis ◽  
Morphological Variation ◽  
Morphological Changes ◽  
The Internet ◽  
Rule Based ◽  
The People ◽  
Stop Word ◽  
Preprocessing Technique ◽  
Better Than

Sentiment analysis is a technique to analyze the people opinion, attitude, sentiment and emotion towards any particular object. Sentiment analysis has the following steps to predict the opinion of a review sentences. The steps are preprocessing, feature selection, classification and sentiment prediction. Preprocessing is the main important step and it consists of many techniques. They are Stop word Removal, punctuation removal, conversion of numbers to number names. Stemming is another important preprocessing technique which is used to transform the words in text into their grammatical root form and is mainly used to improve the retrieval of the information from the internet. It is applied mainly to get strengthen the retrieval of the information. Many morphological languages have immense amount of morphological deviation in the words. It triggered vast challenges. Many algorithms exist with different techniques and has several drawbacks. The aim of this paper is to propose a rule based stemmer that is a truncating stemmer. The new stemming mechanism in this paper has brought about many morphological changes. The new rule based morphological variation removable stemming algorithm is better than the existing other algorithms such as New Porter, Paice/Lovins and Lancaster stemming algorithm

scholarly journals Sentiment Analysis through Machine Learning

2021 ◽  
Vol 56 (3) ◽  
pp. 384-393
Author(s):  
Md. Abbas Ali Khan ◽  
Ali-Emran ◽  
Md. Alamgir Kabir ◽  
Mohammad Hanif Ali ◽  
A. K. M. Fazlul Haque
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Distance ◽  
The Internet ◽  
Text Document ◽  
Ride Sharing ◽  
Wheeled Vehicle ◽  
The People ◽  
Day By Day ◽  
Rainy Days

In recent years, App-Based Transportation System (ABTS) like Ride Sharing (Uber, Patho) has become popular day by day. For our daily life, a rickshaw (a 3-wheeled vehicle usually for one or two passengers that one man pulls) is most important for a short distance. If we add this vehicle to our ABTS system, it will be very much helpful for us, specifically for the rainy season in Bangladesh. On heavy rainy days, in our city Dhaka, other vehicles like CNG, cars, and bikes become unused because roads go underwater. However, the man who pulled the rickshaw can serve this condition. It is more important than the conventional rickshaw is unable to provide such service properly. In this regard, we are proposing an App-Based Rickshaw (ABR), which is convenient to get over distance through the internet. To do this, we have collected data through close questionnaires’ from several types of people. In contrast, collected data are based on a text document. So our aim is to Sentiment Analysis (SA) of the people through machine learning and checks the feasibility of applicability in the real world.

Sentiment Analysis of Social Media as Tool to Improve Customer Retention

2017 ◽  
pp. 635-648
Author(s):  
Wafaa A. Al-Rabayah ◽  
Ahmad Al-Zyoud
Keyword(s):  
Social Media ◽  
Social Network ◽  
Literature Review ◽  
Competitive Advantage ◽  
Sentiment Analysis ◽  
Customer Retention ◽  
Unstructured Data ◽  
Amount Of Information ◽  
Business Applications ◽  
The Web

Sentiment analysis is a process of determining the polarity (i.e. positive, negative or neutral) of a given text. The extremely increased amount of information available on the web, especially social media, create a challenge to be retrieved and analyzed on time, timely analyzed of unstructured data provide businesses a competitive advantage by better understanding their customers' needs and preferences. This literature review will cover a number of studies about sentiment analysis and finds the connection between sentiment analysis of social network content and customers retention; we will focus on sentiment analysis and discuss concepts related to this field, most important relevant studies and its results, its methods of applications, where it can be applied and its business applications, finally, we will discuss how can sentiment analysis improve the customer retention based on retrieved data.

Sentiment Analysis of Social Media as Tool to Improve Customer Retention

2017 ◽  
pp. 207-223
Author(s):  
Wafaa A. Al-Rabayah ◽  
Ahmad Al-Zyoud
Keyword(s):  
Social Media ◽  
Social Network ◽  
Literature Review ◽  
Competitive Advantage ◽  
Sentiment Analysis ◽  
Customer Retention ◽  
Unstructured Data ◽  
Amount Of Information ◽  
Business Applications ◽  
The Web

Sentiment analysis is a process of determining the polarity (i.e. positive, negative or neutral) of a given text. The extremely increased amount of information available on the web, especially social media, create a challenge to be retrieved and analyzed on time, timely analyzed of unstructured data provide businesses a competitive advantage by better understanding their customers' needs and preferences. This literature review will cover a number of studies about sentiment analysis and finds the connection between sentiment analysis of social network content and customers retention; we will focus on sentiment analysis and discuss concepts related to this field, most important relevant studies and its results, its methods of applications, where it can be applied and its business applications, finally, we will discuss how can sentiment analysis improve the customer retention based on retrieved data.

Impact of Deep Learning on Semantic Sentiment Analysis

2021 ◽  
pp. 97-117
Author(s):  
Neha Gupta ◽  
Rashmi Agrawal
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Semantic Technologies ◽  
Sources Of Information ◽  
Accurate Analysis ◽  
Online Social Media ◽  
Semantic Orientation ◽  
Semantically Enhanced

Online social media (forums, blogs, and social networks) are increasing explosively, and utilization of these new sources of information has become important. Semantics plays a significant role in accurate analysis of an emotion speech context. Adding to this area, the already advanced semantic technologies have proven to increase the precision of the tests. Deep learning has emerged as a prominent machine learning technique that learns multiple layers or data characteristics and delivers state-of-the-art output. Throughout recent years, deep learning has been widely used in the study of sentiments, along with the growth of deep learning in many other fields of use. This chapter will offer a description of deep learning and its application in the analysis of sentiments. This chapter will focus on the semantic orientation-based approaches for sentiment analysis. In this work, a semantically enhanced methodology for the annotation of sentiment polarity in Twitter/ Facebook data will be presented.

Big Data, Data Science, and Career Pathways

2020 ◽  
pp. 239-254
Author(s):  
David W. Dorsey
Keyword(s):  
Big Data ◽  
Data Science ◽  
Career Pathways ◽  
Unstructured Data ◽  
Future Application ◽  
The Internet ◽  
The Future ◽  
Skill Requirements ◽  
Enormous Number ◽  
Over Time

With the rise of the internet and the related explosion in the amount of data that are available, the field of data science has expanded rapidly, and analytic techniques designed for use in “big data” contexts have become popular. These include techniques for analyzing both structured and unstructured data. This chapter explores the application of these techniques to the development and evaluation of career pathways. For example, data scientists can analyze online job listings and resumes to examine changes in skill requirements and careers over time and to examine job progressions across an enormous number of people. Similarly, analysts can evaluate whether information on career pathways accurately captures realistic job progressions. Within organizations, the increasing amount of data make it possible to pinpoint the specific skills, behaviors, and attributes that maximize performance in specific roles. The chapter concludes with ideas for the future application of big data to career pathways.

Sign in / Sign up

Export Citation Format

Share Document