scholarly journals Stemming and Lemmatization of Tweets for Sentiment Analysis using R

2019 ◽  
Vol 8 (2) ◽  
pp. 2038-2040
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Text Analysis ◽  
Rapid Rate ◽  
Volume Data ◽  
Huge Amount ◽  
Manual Analysis ◽  
User Communities ◽  
Use Of Social Media ◽  
Day By Day

In our digital India, the use of social media like twitter, blogs and various forums is growing with the rapid rate. Thus the size of the data is becoming big day by day and in the span of this type of high varied and volume data, manual analysis would be a clumsy job. So, there is an alarming rate to analyze that large amount of data to make it suitable for analysis purpose. As a most elaborate open source platform, R has immeasurable user communities that thrives and perpetuate a huge amount of text analysis packages. So, in this paper we are analyzing movie related tweets using machine learning in R.

scholarly journals Suicide Prediction on Social Media by Implementing Sentiment Analysis along with Machine Learning

2019 ◽  
Vol 8 (2) ◽  
pp. 4833-4837
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Semantic Analysis ◽  
Machine Learning Techniques ◽  
Suicide Prediction ◽  
The People ◽  
Learning Techniques ◽  
Use Of Social Media ◽  
Day By Day

Technology is growing day by day and the influence of them on our day-to-day life is reaching new heights in the digitized world. Most of the people are prone to the use of social media and even minute details are getting posted every second. Some even go to the extent of posting even suicide related issues. This paper addresses the issue of suicide and is predicting the suicide issues on social media and their semantic analysis. With the help of Machine Learning techniques and semantic analysis of sentiments the prediction and classification of suicide is done. The model of approach is a four-tier approach, which is very beneficial as it uses the twitter4J data by using weka tool and implementing it on WordNet. The precision and accuracy aspects are verified as the parameters for the performance efficiency of the procedure. We also give a solution for the lack of resources regarding the terminological resources by providing a phase for the generation of records of vocabulary also.

Exploring fake news identification using word and sentence embeddings

2021 ◽  
pp. 1-8
Author(s):  
V.T Priyanga ◽  
J.P Sanjanasri ◽  
Vijay Krishna Menon ◽  
E.A Gopalakrishnan ◽  
K.P Soman
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Network Analysis ◽  
Supervised Machine Learning ◽  
Breeding Ground ◽  
Fake News ◽  
Data Set ◽  
Highly Correlated ◽  
Use Of Social Media ◽  
The Liar

The widespread use of social media like Facebook, Twitter, Whatsapp, etc. has changed the way News is created and published; accessing news has become easy and inexpensive. However, the scale of usage and inability to moderate the content has made social media, a breeding ground for the circulation of fake news. Fake news is deliberately created either to increase the readership or disrupt the order in the society for political and commercial benefits. It is of paramount importance to identify and filter out fake news especially in democratic societies. Most existing methods for detecting fake news involve traditional supervised machine learning which has been quite ineffective. In this paper, we are analyzing word embedding features that can tell apart fake news from true news. We use the LIAR and ISOT data set. We churn out highly correlated news data from the entire data set by using cosine similarity and other such metrices, in order to distinguish their domains based on central topics. We then employ auto-encoders to detect and differentiate between true and fake news while also exploring their separability through network analysis.

Does Social Media Really Help?

2016 ◽  
Vol 6 (3) ◽  
pp. 15-33
Author(s):  
Rebecca Liu ◽  
Aysegul Eda Kop
Keyword(s):  
Social Media ◽  
New Product Development ◽  
New Product ◽  
Evaluation Tool ◽  
Huge Amount ◽  
Moderation Effect ◽  
Customer Involvement ◽  
Product Success ◽  
Use Of Social Media ◽  
Critical Literature

This paper intends to understand whether social media helps new product success in the context of customer involvement. The authors present a debate about the opportunities and challenges of using social media in new product development (NPD). Through a critical literature review, a conceptual model with a moderation effect is presented. The review is mainly derived from 286 relevant papers published in top-ranked journals between 2005 and 2014. The results suggest that while social media provides an effective and efficient method for collecting information and knowledge about customers' expectations and experiences, it does not necessarily always lead to NPD success. The study shows that hidden customer needs, an advanced evaluation tool, the huge amount of information and a firm's absorptive capacity challenge the use of social media.

scholarly journals Spam Detection Framework using ML Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 5326-5329
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Information Sharing ◽  
Detection System ◽  
Reliable Data ◽  
Machine Learning Algorithms ◽  
Spam Detection ◽  
Function Type ◽  
Use Of Social Media

The current use of social media has created incomparable amounts of social data, as it is a cheap and popular information sharing communication platform. Nowadays, a huge percentage of people depend on the accessible material on social networking in their choices (e.g. comments and suggestions about a subject or product). This feature on exchanging knowledge with a wide number of users has quickly prompted social spammers to exploit the network of confidence to distribute spam messages and support personal forums, advertising, phishing, scams and so on. Identifying these spammers and spam material is a hot subject of study, and while large amounts of experiments have recently been conducted to this end, so far the methodologies are only barely able to identify spam feedback, and none of them demonstrates the value of each derived function type. In this study, we have suggested a machine learning-based spam detection system that determines whether or not a specific message in the dataset is spam using a set of machine learning algorithms. Four main features have been used; including user-behavioral, user-linguistic, reviewbehavioral and review-linguistic, to improve the spam detection process and to gather reliable data

Need for Making Use of Social Media Channels for Open Innovation Management

2018 ◽  
pp. 92-102
Keyword(s):  
Social Media ◽  
Open Innovation ◽  
Innovation Management ◽  
Online Communication ◽  
Good Opportunity ◽  
The Public ◽  
User Communities ◽  
Business Entities ◽  
Use Of Social Media ◽  
Media Channels

Social media has exploded as a category of online communication where people create content, share it, bookmark it, and network at an exponential rate. Social media is changing the public interaction in society. It is setting new trends and agendas in topics. The enormity and high variety of information propagates through large user communities. It provides a good opportunity for harnessing that data for specific purposes. This chapter talks about the usefulness of interaction on social media channels for finding an open, innovative approach in solving issues faced by business entities and rural people in India.

scholarly journals The Booming Social Media and Increasing Perceptual Loneliness: The Scientific Co-Relational Study

2016 ◽  
Vol 3 (4) ◽  
Author(s):  
Dr. Prashant G. Sonawanen ◽  
Dr. Karuna S. Wankhade
Keyword(s):  
Social Media ◽  
Variable Ratio ◽  
Age Group ◽  
Positive Correlation ◽  
Adult Age ◽  
Loneliness Scale ◽  
The Social ◽  
The World ◽  
Use Of Social Media ◽  
Day By Day

Day by day the world is coming closer and closer, and the communication is becoming super quick than ever, just at the finger tips of a person. And social media played an important role in it, where you can communicate with the group at a time whenever and wherever you wish so. This has done marvelous change in the communication of people and it proved great asset, but at the same time it is creating a sort of virtual and superficial bonds in the relations which are not able to give the feel of connectedness in a true sense. Many of the studies that are done shows that, the social mediums which are opening communication on the broad platform, but at the same time creating the loneliness feeling deep within and which is increasing day by day. So the present study attempts to understand the co-relation between these two issues scientifically. Present study is done with the help of standardized perceptual Loneliness scale by Dr. Praveen Kumar Zha, and a Non-standardized scale for measuring the extent of the use of social media constructed by researcher. Samples are the individuals from adolescent to adult age group, of various professions, who are using the social media in significant and variable ratio. Result of the study concluded that there is positive correlation in both these variables. Implications and suggestions for the study are highlighted.  Suggestions for further study are offered.

scholarly journals Identification and Detection of Cyberbullying on Facebook Using Machine Learning Algorithms

2021 ◽  
Vol 23 (4) ◽  
pp. 1-21
Author(s):  
Nureni Ayofe AZEEZ ◽  
Sanjay Misra ◽  
Omotola Ifeoluwa LAWAL ◽  
Jonathan Oluranti
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Chi Square ◽  
Social Media Platforms ◽  
Bayes Algorithm ◽  
Use Of Social Media

The use of social media platforms such as Facebook, Twitter, Instagram, WhatsApp, etc. have enabled a lot of people to communicate effectively and frequently with each other and this has enabled cyberbullying to occur more frequently while using these networks. Cyberbullying is known to be the cause of some serious health issues among social media users and creating a way to identify and detect this holds significant importance. This paper takes a look at unique features gotten from the Facebook dataset and develops a model that identifies and detect cyberbullying posts by applying machine learning algorithms (Naïve Bayes Algorithm and K-Nearest Neighbor). The project also uses a feature selection algorithm namely x2 test (Chi-Square test) to select important features which can improve the performance of the classifiers and decrease classification time. The result of this paper tends to detect cyberbullying in Facebook with a high degree of accuracy and also improve the performance of the machine learning classifiers.

scholarly journals Bi-lingual Information Retrieval System for English and Italian Tweets using Python

2019 ◽  
Vol 8 (6) ◽  
pp. 1132-1135
Keyword(s):  
Information Retrieval ◽  
Text Classification ◽  
Text Analysis ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Retrieval Model ◽  
Dynamic Information ◽  
Heat Map ◽  
Use Of Social Media ◽  
Day By Day

Bi-lingual text analysis is competent in present scenario as the information gathered in various languages is flattering. The bi-lingual text classification is yet an obscure area whereas the text classification in a single language is well known. The concept of bi-lingual text has been left in a shell, apart from the lame stream of both theory as well as practical. The use of social media is increasing day by day and thus the amount of data too in increasing with a rapid rate. So, it is an alarming stage to analyze the big data and extract the useful information. In this paper, we are developing a dynamic information retrieval model and extricating the sentiments of people on global warming of English and Italian tweets and corresponding to it its heat map and affinity map are generated as it produces the output after harmonizing different objects which diverge in the rung of relevancy to the question

scholarly journals نوجوانوں میں خودکشی کا بڑھتا ہوا رجحان، اسباب اور اس کا حل سیرت طیبہ کی روشنی میں

rahatulquloob ◽  
2019 ◽  
Vol 3 (2(2)) ◽  
pp. 63-74
Author(s):  
Dr. Shazia Ramzan ◽  
Dr. Sabeen Akbar
Keyword(s):  
Social Sciences ◽  
Social Media ◽  
Young Girls ◽  
Islamic Country ◽  
The Social ◽  
Increasing Trend ◽  
Use Of Social Media ◽  
Day By Day

Suicide is strictly forbidden in Islam. But now a days we can see that ratio of suicide is increasing day by day in our society and this increasing ratio of suicide is mostly among youngsters. Being an Islamic country and being Muslims, it is an alarming situation for all of us and a moment to stop and think that why our youth is involving in this haram act .We conducted a survey among youngsters. We constructed a questionnaire. We asked about the causes, factors increasing this trend, main cause and steps to reduce this ratio. Our respondents were young girls. So after filling questionnaire we came to know that one of the biggest cause of increasing trend of suicide is gap from Islamic Teachings. And some other causes are poverty, unemployment, laziness of parents in supervision of children, use of social media, and failure in exam. So at end we concluded that we follow Islamic teachings properly and spend our life according to the teaching of Islam then this ratio can be decreased. We used SPSS statistical package for the social sciences for analysis of this data.

scholarly journals A Algorithm for Emotion Prediction using Twitter Dataset

2019 ◽  
Vol 8 (4) ◽  
pp. 7243-7246
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Weight Matrix ◽  
Edge Weight ◽  
Learning Approach ◽  
Huge Amount ◽  
A Algorithm ◽  
Search Terms ◽  
Machine Learning Approach ◽  
Novel Algorithm

In today's internet world almost each and everyone uses Smartphone and they are all also active in various social media. In general social media contains a huge amount of data that can be extracted and utilized to find various data insights including polarity emotion etc... This research paper mainly investigates in emotion predection using a machine learning approach . Here a novel algorithm was introduced to predict the emotion of tweets . The algorithm mainly deals with emotion Prediction by utilizing various parameters like unigram , bigram , edge weight matrix , frequency matrix and so on . Finally , the result was predicted with the emotions of the tweets . While testing with various search terms this algorithm performs well in Predicting the emotion like anger, happiness and so on .

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