scholarly journals Heterogeneous Feature Analysis on Twitter Data Set for Identification of Spam Messages

2022 ◽  
Author(s):  
Valliyammai Chinnaiah ◽  
Cinu C Kiliroor
Keyword(s):  
Social Networks ◽  
Feature Extraction ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Machine Learning Algorithms ◽  
Feature Analysis ◽  
Data Set ◽  
Twitter Data ◽  
Heterogeneous Feature

Spam is an undesirable content that present on online social networking sites, while spammers are the users who post this content on social networking sites. Unwanted messages posted on Twitter may have several goals and the spam tweets can interfere with statistics presented by Twitter mining tools and squander users’ attention.. Since Twitter has achieved a lot of attractiveness through-out the world, the interest towards it by the spammers and malevolent users is also increases. To overcome the spam problems many researchers proposed ideas using machine learning algorithms for the identification of spam messages. Not only the selection of classifiers but also the variegated feature analysis is essential for the identification of irrelevant messages in social networks. The proposed model performs a heterogeneous feature analysis on the twitter data streams for classifying the unsolicited messages using binary and continuous feature extraction with sentiment analysis on social network datasets. The features created are assessed using significant stratagems and the finest features are selected. A classifier model is built using these feature vectors to predict and identify the spam messages in Twitter. The experimental results clearly show that the proposed Sentiment Analysis based Binary and Continuous Feature Extraction model with Random Forest (SA-BC-RF) approach classifies the spam messages from the social networks with an accuracy of 90.72% when compared with the other state-of-the-art methods.

scholarly journals A Survey on Analysis of Twitter Opinion Mining Using Sentiment Analysis

2019 ◽  
pp. 537-542
Author(s):  
Vishnu VardanReddy ◽  
Mahesh Maila ◽  
Sai Sri Raghava ◽  
Yashwanth Avvaru ◽  
Sri. V. Koteswarao
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Online Communication ◽  
Valuable Insight ◽  
Huge Amount ◽  
Twitter Data ◽  
Product Service ◽  
Different Types

In recent years, there is a rapid growth in online communication. There are many social networking sites and related mobile applications, and some more are still emerging. Huge amount of data is generated by these sites everyday and this data can be used as a source for various analysis purposes. Twitter is one of the most popular networking sites with millions of users. There are users with different views and varieties of reviews in the form of tweets are generated by them. Nowadays Opinion Mining has become an emerging topic of research due to lot of opinionated data available on Blogs & social networking sites. Tracking different types of opinions & summarizing them can provide valuable insight to different types of opinions to users who use Social networking sites to get reviews about any product, service or any topic. Analysis of opinions & its classification on the basis of polarity (positive, negative, neutral) is a challenging task. Lot of work has been done on sentiment analysis of twitter data and lot needs to be done. In this paper we discuss the levels, approaches of sentiment analysis, sentiment analysis of twitter data, existing tools available for sentiment analysis and the steps involved for same. Two approaches are discussed with an example which works on machine learning and lexicon based respectively.

A New SVM Method for Recognizing Polarity of Sentiments in Twitter

2017 ◽  
pp. 281-291 ◽  
Author(s):  
Sanjiban Sekhar Roy ◽  
Marenglen Biba ◽  
Rohan Kumar ◽  
Rahul Kumar ◽  
Pijush Samui
Keyword(s):  
Social Networks ◽  
Support Vector Machine ◽  
Public Relations ◽  
Social Networking ◽  
Sentiment Analysis ◽  
High Accuracy ◽  
Support Vector ◽  
Online Social Networking ◽  
Web Page ◽  
Twitter Data

Online social networking platforms, such as Weblogs, micro blogs, and social networks are intensively being utilized daily to express individual's thinking. This permits scientists to collect huge amounts of data and extract significant knowledge regarding the sentiments of a large number of people at a scale that was essentially impractical a couple of years back. Therefore, these days, sentiment analysis has the potential to learn sentiments towards persons, object and occasions. Twitter has increasingly become a significant social networking platform where people post messages of up to 140 characters known as ‘Tweets'. Tweets have become the preferred medium for the marketing sector as users can instantly indicate customer success or indicate public relations disaster far more quickly than a web page or traditional media does. In this paper, we have analyzed twitter data and have predicted positive and negative tweets with high accuracy rate using support vector machine (SVM).

Enhancements in Decision Making Through Sentiment Analysis of Twitter Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4083-4091
Author(s):  
Jagadish S. Kallimani ◽  
S. H. Ajeya ◽  
D. Keerthana ◽  
Manoj J. Shet ◽  
Prasada Hegde
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Social Networking Sites ◽  
Multi Layer Perceptron ◽  
The Public ◽  
Twitter Data ◽  
Product Service ◽  
Reliable Source ◽  
Computational Identification

All trades and business run predominantly on customer satisfaction and serves as the key to success. Usually, the decisions made by people is largely dependent on others’ perspectives. Hence, it becomes important to have reviews in your favor to sustain and outperform competitors in the market. Collecting reviews and predictions and analyzing them is an effective method to get insights on how the product, service or subject is accepted by the public. It also helps us discover the fields or aspects that needs to be improved. This comes under the field of Sentiment Analysis which refers to the computational identification of views, perspectives, opinions and emotions from text and speech through Natural Language Processing. With the emergence of the internet, blogging and social-networking sites are a rage. Twitter is one of the popular and ubiquitous sites and acts as a reliable source of feedback. In this paper, we seek to detect the emotion portrayed in a given tweet with significant accuracy. We propose the use of Word2Vec model and Count Vectorizer to extract features from pre-processed data. The output is fed to trained Multi-Layer Perceptron classifier to detect the emotion behind the sentence.

scholarly journals Online Shopping and Social Media: Friends or Foes?

2017 ◽  
Vol 81 (6) ◽  
pp. 24-41 ◽  
Author(s):  
Yuchi Zhang ◽  
Michael Trusov ◽  
Andrew T. Stephen ◽  
Zainab Jamal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Social Networking Sites ◽  
Online Shopping ◽  
Negative Relationship ◽  
Data Set ◽  
Marginal Value ◽  
One Year ◽  
The One

As social network use continues to increase, an important question for marketers is whether consumers’ online shopping activities are related to their use of social networks and, if so, what the nature of this relationship is. On the one hand, spending time on social networks could facilitate social discovery, meaning that consumers “discover” or “stumble upon” products through their connections with others. Moreover, cumulative social network use could expose consumers to new shopping-related information, possibly with greater marginal value than the incremental time spent on a shopping website. This process may therefore be associated with increased shopping activity. On the other hand, social network use could be a substitute for other online activities, including shopping. To test the relationship between social network use and online shopping, the authors leverage a unique consumer panel data set that tracks people's browsing of shopping and social network websites and their online purchasing activities over one year. The authors find that greater cumulative usage of social networking sites is positively associated with shopping activity. However, they also find a short-term negative relationship, such that immediately after a period of increased usage of social networking sites, online shopping activity appears to be lower.

scholarly journals A Dictionary Based Analysis of User’s Sentiment Regarding Indian Premier League

2019 ◽  
Vol 8 (11) ◽  
pp. 963-965
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Huge Amount ◽  
Text Format ◽  
Large Source ◽  
Twitter Data ◽  
Premier League

The growth of social media has provided the users with a platform to express their views on numerous themes. Social networking sites like Twitter are considered as large source of users’ sentiment. Twitter has become one of the biggest sources for evaluating sentiment analysis. The shorter and informal nature of the text encourages the users to express their sentiment fast and effectively. The huge amount of data that gets generated mostly in text format can be used for studying user’s sentiment regarding any topic. Indian Premier League (IPL) is a cricket tournament of T20 format that draws a lot of attention from the viewers. Right from the very beginning IPL has remained in the glare for consecutive 12 years. Because of the participation of renowned players from throughout the globe, some famous Bollywood personalities and businessmen, this tournament remains one of the topics for discussion. In this paper, we propose to study the users sentiment related to IPL using twitter data. The tweets related to IPL are proposed to be downloaded and analyzed to find out the sentiment regarding IPL.

A Review on Sentiment Classification: Natural Language Understanding

2019 ◽  
Vol 13 (1) ◽  
pp. 20-27 ◽  
Author(s):  
Srishty Jindal ◽  
Kamlesh Sharma
Keyword(s):  
Natural Language ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Natural Language Understanding ◽  
Business Analytics ◽  
Language Understanding ◽  
Text Data ◽  
Data Set ◽  
Market Positioning ◽  
Illegal Activities

Background: With the tremendous increase in the use of social networking sites for sharing the emotions, views, preferences etc. a huge volume of data and text is available on the internet, there comes the need for understanding the text and analysing the data to determine the exact intent behind the same for a greater good. This process of understanding the text and data involves loads of analytical methods, several phases and multiple techniques. Efficient use of these techniques is important for an effective and relevant understanding of the text/data. This analysis can in turn be very helpful in ecommerce for targeting audience, social media monitoring for anticipating the foul elements from society and take proactive actions to avoid unethical and illegal activities, business analytics, market positioning etc. Method: The goal is to understand the basic steps involved in analysing the text data which can be helpful in determining sentiments behind them. This review provides detailed description of steps involved in sentiment analysis with the recent research done. Patents related to sentiment analysis and classification are reviewed to throw some light in the work done related to the field. Results: Sentiment analysis determines the polarity behind the text data/review. This analysis helps in increasing the business revenue, e-health, or determining the behaviour of a person. Conclusion: This study helps in understanding the basic steps involved in natural language understanding. At each step there are multiple techniques that can be applied on data. Different classifiers provide variable accuracy depending upon the data set and classification technique used.

scholarly journals COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis

2021 ◽  
pp. 1-13
Author(s):  
Usman Naseem ◽  
Imran Razzak ◽  
Matloob Khushi ◽  
Peter W. Eklund ◽  
Jinman Kim
Keyword(s):  
Sentiment Analysis ◽  
Large Scale ◽  
Data Set ◽  
Twitter Data

MARKETING COMMUNICATION STRATEGIES OF THE ENTERPRISE IN THE CONDITIONS OF ECONOMY DIGITALIZATION

2018 ◽  
Author(s):  
Anastasiia Zerkal ◽  
◽  
Viktoriia Holomb ◽  
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Social Networking Sites ◽  
Positive Impact ◽  
Communication Strategies ◽  
Mobile Technologies ◽  
Customer Engagement ◽  
User Engagement ◽  
Marketing Communication ◽  
Internet Users

The article considers the peculiarities of the formation of marketing communication strategies of the enterprise in terms of digitalization of the economy. The main directions of mass media in the twentieth century are determined and the delimitation of modern social media is presented. The conditions of compliance of the website have been determined so that it can be considered as a part of web 2.0: the ability to independently contribute to the content of the site; User control of your own information and website design -interactive and useful. The influence of digital and mobile technologies on the peculiarities of users' communication, as well as their attitude to the interactivity of social networks is proved. The potential of social networks to support their brands, increase the customer base and promote goods and services of enterprises has been identified. It is determined that due to its popularity, social networking sites have had a significant impact on ways of social communication and as a result have changed the sales channels of enterprises. It is estimated that the number of people using social networks is growing very fast, and at the time of writing, more than 2.62 billion people are using social networking sites. The largest social networks were analyzed: Facebook, Pinterest, Twitter, LinkedIn, Instagram. Their features and advantages for both users and professional marketers of enterprises are determined. It is estimated that in 2021, 71% of the total number of Internet users were users of social networks, and this percentage is projected to increase. The most popular activity among Internet users is social networking, and it has a high level of user engagement, which has a positive impact on the sales of businesses that work with digital marketing tools. The ease and low cost of Internet marketing compared to conventional advertising has proven that businesses in all sectors of the economy can more effectively reach their target audience, and social networks help influence other potential customers, and allow businesses to get useful feedback on their product or service. Ultimately, this leads to improved products / services and customer engagement, ie improves the company's marketing communication strategies in today's digital economy.

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