scholarly journals Cooking Is Creating Emotion: A Study on Hinglish Sentiments of Youtube Cookery Channels Using Semi-Supervised Approach

2019 ◽  
Vol 3 (3) ◽  
pp. 37 ◽  
Author(s):  
Gagandeep Kaur ◽  
Abhishek Kaushik ◽  
Shubham Sharma
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Unsupervised Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Term Frequency ◽  
Learning Technique ◽  
Non Parametric

The success of Youtube has attracted a lot of users, which results in an increase of the number of comments present on Youtube channels. By analyzing those comments we could provide insight to the Youtubers that would help them to deliver better quality. Youtube is very popular in India. A majority of the population in India speak and write a mixture of two languages known as Hinglish for casual communication on social media. Our study focuses on the sentiment analysis of Hinglish comments on cookery channels. The unsupervised learning technique DBSCAN was employed in our work to find the different patterns in the comments data. We have modelled and evaluated both parametric and non-parametric learning algorithms. Logistic regression with the term frequency vectorizer gave 74.01% accuracy in Nisha Madulika’s dataset and 75.37% accuracy in Kabita’s Kitchen dataset. Each classifier is statistically tested in our study.

scholarly journals Deep Learning Technique to Predict Heart Disease using IoT Based ECG Data

2019 ◽  
Vol 9 (2) ◽  
pp. 2559-2562
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Rule Based ◽  
Learning Techniques ◽  
Learning Technique ◽  
Logistic Regression Algorithm ◽  
Target Output

Scientific Knowledge and Electronic devices are growing day by day. In this aspect, many expert systems are involved in the healthcare industry using machine learning algorithms. Deep neural networks beat the machine learning techniques and often take raw data i.e., unrefined data to calculate the target output. Deep learning or feature learning is used to focus on features which is very important and gives a complete understanding of the model generated. Existing methodology used data mining technique like rule based classification algorithm and machine learning algorithm like hybrid logistic regression algorithm to preprocess data and extract meaningful insights of data. This is, however a supervised data. The proposed work is based on unsupervised data that is there is no labelled data and deep neural techniques is deployed to get the target output. Machine learning algorithms are compared with proposed deep learning techniques using TensorFlow and Keras in the aspect of accuracy. Deep learning methodology outfits the existing rule based classification and hybrid logistic regression algorithm in terms of accuracy. The designed methodology is tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal beats. The proposed approach based on deep learning technique offered a better performance, improving the results when compared to machine learning approaches of the state-of-the-art

scholarly journals A Proportional Sentiment Analysis of MOOCs Course Reviews Using Supervised Learning Algorithms

2021 ◽  
Vol 26 (5) ◽  
pp. 501-506
Author(s):  
Anuj Kumar Singh ◽  
Sandeep Kumar ◽  
Shashi Bhushan ◽  
Pramod Kumar ◽  
Arun Vashishtha
Keyword(s):  
Logistic Regression ◽  
Sentiment Analysis ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Regression Function ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Assessment Task ◽  
Supervised Learning Algorithms

When anyone is looking to enroll for a freely available online course so the first and famous name comes in front of the searcher is MOOC courses. So here in this article our focus is to collect the comments by enrolled users for the specified MOOC course and apply sentiment analysis over that data. The significance of our article is to introduce a proficient sentiment analysis algorithm with high perceptive execution in MOOC courses, by seeking after the standards of gathering various supervised learning methods where the performance of various supervised machine learning algorithms in performing sentiment analysis of MOOC data. Some research questions have been addressed on sentiment analysis of MOOC data. For the assessment task, we have investigated a large no of MOOC courses, with the different Supervised Learning methods and calculated accuracy of the data by using parameters such as Precision, Recall and F1 Score. From the results we can conclude that when the bigram model was applied to the logistic regression, the Multilayer Perceptron (MLP) overcomes the accuracy by other algorithms as SVM, Naive Bayes and achieved an accuracy of 92.44 percent. To determine the sentiment polarity of a sentence, the suggested method use term frequency (No of Positive, Negative terms in the text) to calculate the sentiment polarity of the text. We use a logistic regression Function to predict the sentiment classification accuracy of positive and negative comments from the data.

scholarly journals To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

2019 ◽  
Vol 8 (12) ◽  
pp. 4433-4435
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Social Media Text ◽  
The Impact

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mona Bokharaei Nia ◽  
Mohammadali Afshar Kazemi ◽  
Changiz Valmohammadi ◽  
Ghanbar Abbaspour
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Wearable Devices ◽  
Machine Learning Algorithms ◽  
Content Type ◽  
Iot Devices ◽  
The Right ◽  
Media Sentiment

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

scholarly journals Sentiment Analysis on the Ratification of Penghapusan Kekerasan Seksual Bill on Twitter

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
Gary Dimitri Hamidi ◽  
Farida Afira Bestari ◽  
Alexandra Situmorang ◽  
Nur Aini Rakhmawati
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Sexual Violence ◽  
Sentiment Analysis ◽  
Confusion Matrix ◽  
Support Vector ◽  
Public Sentiment ◽  
Victim Protection ◽  
The Republic ◽  
Forms Of Violence

Rancangan Undang-Undang Penghapusan Kekerasan Seksual (RUU PKS), or the bill of the Republic of Indonesia on the Elimination of Sexual Violence, is a bill that discusses sexual violence, victim protection and its scope concern the matters related to sexual violence. Elimination of sexual violence according to the PKS Bill aims to prevent all forms of violence. These discussions and conversations also occur on social media, especially on Twitter. Taking public sentiment is significant in choosing the proper messages, interference, and policy. Sentiment analysis is a field of study that analyses opinions, sentiments, judgments, evaluations of a person attitudes and emotions regarding a particular topic, service, product, individual, organization or activity. This study used the method of crawling to get data from Twitter. Then data cleansing, data processing is carried out using Bernoulli, Logistic Regression, and Support Vector Classification (SVC) algorithm. The data is then evaluated using three methods: accuracy, classification report, and confusion matrix. Based on the three algorithms used, it is found that all methods are equally accurate with 0.65. This study found positive, negative, and neutral sentiments expressed to the bill of Elimination of Sexual Violence through comments. It is shown that most people using the keyword “RUU PKS” are positive to the bill of Elimination of Sexual Violence (RUU PKS) while most people’s sentiments using #RUUPKSBukanSolusi are negative to the bill.

scholarly journals A Comparative Study of Different Classification Techniques for Sentiment Analysis

2020 ◽  
Vol 11 (1) ◽  
pp. 49-57
Author(s):  
Soumadip Ghosh ◽  
Arnab Hazra ◽  
Abhishek Raj
Keyword(s):  
Social Media ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Decision Tree ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Support Vector ◽  
Product Reviews ◽  
Accurate Model

Sentiment analysis denotes the analysis of emotions and opinions from text. The authors also refer to sentiment analysis as opinion mining. It finds and justifies the sentiment of the person with respect to a given source of content. Social media contain vast amounts of the sentiment data in the form of product reviews, tweets, blogs, and updates on the statuses, posts, etc. Sentiment analysis of this largely generated data is very useful to express the opinion of the mass in terms of product reviews. This work is proposing a highly accurate model of sentiment analysis for reviews of products, movies, and restaurants from Amazon, IMDB, and Yelp, respectively. With the help of classifiers such as logistic regression, support vector machine, and decision tree, the authors can classify these reviews as positive or negative with higher accuracy values.

scholarly journals Sentimental Analysis and Detection of Rumours for Social Media Data using Logistic Regression

2019 ◽  
Vol 9 (1) ◽  
pp. 2123-2126
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Sentiment Analysis ◽  
User Generated Content ◽  
Ancient Text ◽  
Social Media Data ◽  
Social Media Platform ◽  
Subjective Knowledge ◽  
Media Platform ◽  
Media Data

Over the last decade ,the Internet has become an ubiquitous and enormous suffuse medium of the user generated content and self-opinionated knowledge. Users currently have the facility to specify their views, opinions and ideas publically. Victimizing social media platform is a place where people can express their mindsets and feelings in a well associated manner and hence is productive and economical . These ever-growing subjective knowledge are doubtless, an especially made for supply of data of any reasonably method process. The Sentiment Analysis aims at distinctive self-opinionated knowledge during an Internet and classifying them in line with their polarity whether or not they contain positive ,negative or neutralizing references. Sentiment Analysis could be a drawback of text based mostly analysis however there are difficulties which are needed to be pondered upon that would create a tough parameter as compared to ancient text based analysis. It depicts the state where it has a desire of trial to figure out these issues and it's spread out many chances for further analysis for handling negative sentences, hidden emotions , slangs and sentence sarcasm. The project also proposes additional features compared to other previous model projects by enabling the detection of rumor , identifying and analyzing whether message given via user belongs to rumor category or not using Logistic Regression process in Machine Learning domain.

scholarly journals Text Polarity Detection using Multiple Supervised Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 1612-1618
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
The Public ◽  
Social Media Platforms ◽  
Day By Day

Sentiment analysis is the classifying of a review, opinion or a statement into categories, which brings clarity about specific sentiments of customers or the concerned group to businesses and developers. These categorized data are very critical to the development of businesses and understanding the public opinion. The need for accurate opinion and large-scale sentiment analysis on social media platforms is growing day by day. In this paper, a number of machine learning algorithms are trained and applied on twitter datasets and their respective accuracies are determined separately on different polarities of data, thereby giving a glimpse to which algorithm works best and which works worst..

scholarly journals Pengembangan Algoritma Unsupervised Learning Technique Pada Big Data Analysis di Media Sosial sebagai media promosi Online Bagi Masyarakat

2019 ◽  
Vol 12 (1) ◽  
pp. 79-96
Author(s):  
Nurhayati Buslim
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analysis ◽  
Unsupervised Learning ◽  
Support System ◽  
Big Data Analysis ◽  
Combine Data ◽  
Software Applications ◽  
Sample Data ◽  
Learning Technique

ABSTRAKKumpulan data yang besar atau dikenal dengan istilah big data dapat dianalisis dengan berbagai macam teknik. Salah satu teknik untuk mengolah big data adalah Unsupervised Technique. Ada berbagai macam algoritma yang menerapkan teknik ini. Setiap algoritma memiliki cara dan karakteristik masing-masing. Penelitian ini berfokus pada pengembagan algoritma yang menerapkan unsupervised learning technique salah satunya algoritma K-Means dengan mengambil sample data pada masyarakat yang melakukan usaha kreatif dan mandiri. Masyarakat dalam yang memanfaatkan usaha online dan offline dalam pemasarannya. Peneliti melakukan uji eksperimen dan simulasi terhadap algoritma tersebut dengan menghasilkan output berupa aplikasi software serta tabel dan grafik yang mampu menggabungkan data yang didapat dari media social dan kuesioner secara ofline. Hasil analisa pengolahan Data tersebut dapat di gunakan sebagao DSS (Decicion Support System) oleh masyarakat dalam mengambil keputusan pengembangan pemasaran produksinya selanjutnya.  ABSTRACTLarge data collection or known as big data can be analyzed with various techniques. One technique for processing big data is Unsupervised Technique. There are various kinds of algorithms that apply this technique. Each algorithm has its own ways and characteristics. This study focuses on developing an algorithm that implements an unsupervised learning technique, one of which is the K-Means algorithm by taking data samples to people who are doing creative and independent efforts. The Society utilized online and offline business in marketing. The researcher conducted an experimental test and simulation of the algorithm by producing output in the form of software applications as well as tables and graphs that were able to combine data obtained from social media and questionnaires fromline. The results of the analysis of data processing can be used as a DSS (Decion Support System) by the community in making their next production marketing development decisions. 

Sign in / Sign up

Export Citation Format

Share Document