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.

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.

Network Text Analysis and Sentiment Analysis

2016 ◽  
pp. 137-153
Author(s):  
Veronica Ravaglia ◽  
Luca Zanazzi ◽  
Elvis Mazzoni
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Social Networking Sites ◽  
Word Of Mouth ◽  
Network Text Analysis ◽  
Online Conversations ◽  
Web Forums

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.

scholarly journals Detection and Analysis of Stress using Machine Learning Techniques

2019 ◽  
Vol 9 (1) ◽  
pp. 335-342
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Text Messages ◽  
Word Sense ◽  
Severe Problem ◽  
Sense Disambiguation

Every year tens of millions of people suffer from depression and few of them get proper treatment on time. So, it is crucial to detect human stress and relaxation automatically via social media on a timely basis. It is very important to detect and manage stress before it goes into a severe problem. A huge number of informal messages are posted every day in social networking sites, blogs and discussion forums. This paper describes an approach to detect the stress using the information from social media networking sites, like tweeter.This paper presents a method to detect expressions of stress and relaxation on tweeter dataset i.e. working on sentiment analysis to find emotions or feelings about daily life. Sentiment analysis works the automatic extraction of sentiment related information from text. Here using TensiStrengthframework for sentiment strength detection on social networking sites to extract sentiment strength from the informal English text. TensiStrength is a system to detect the strength of stress and relaxation expressed in social media text messages. TensiStrength uses a lexical approach and a set of rules to detect direct and indirect expressions of stress or relaxation. This classifies both positive and negative emotions based on the strength scale from -5 to +5 indications of sentiments. Stressed sentences from the conversation are considered &categorised into stress and relax. TensiStrength is robust, it can be applied to a widevarietyofdifferent social web contexts. Theeffectiveness of TensiStrength depends on the nature of the tweets.In human being there is inborn capability to differentiate the multiple senses of an ambiguous word in a particular context, but machine executes only according to the instructions. The major drawback of machine translation is Word Sense Disambiguation. There is a fact that a single word can have multiple meanings or "senses." In the pre-processing partof-speech disambiguation is analysed and the drawback of WSD overcomes in the proposed method by unigram, bigram and trigram to give better result on ambiguous words. Here, SVM with Ngram gives better resultPrecision is65% and Recall is 67% .But, the main objective of this technique is to find the explicit and implicit amounts of stress and relaxation expressed in tweets. Keywords: Stress Detection, Data Mining, TensiStrength, word sense disambiguation.

Computing Opinions for Twitter Review Data

2020 ◽  
Vol 17 (9) ◽  
pp. 4360-4363
Author(s):  
S. Tenkale Pallavi ◽  
S. Jagannatha
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Web Pages ◽  
Human Being ◽  
Association Network ◽  
The Social ◽  
Individual Human

Customers and users post their opinions or reviews on social networking sites and it has increased the amount of data WWW. With this users from all over world try to share their opinions and sentiments on the blogging sites every day. Internet is being used in form of web pages, social media, and sometimes blogs which increases online portals sentiments, reviews, opinions, references, scores, and feedbacks are also generated by people. Twitter is the most famous micro-blogging site where users express their opinions in the form of tweets. The user can express their sentiments about various aspects e.g., books, celebrities, restaurants, various products, research, events, etc. All these opinions plays vital roles and they are quite important for various businesses, for government schemes, and for individual human being as well. Still, there are many curbs in mining reviews or opinions and process to calculate them. These limitations have turned into highland in investigating the actual gist of opinions and measuring its polarity. Hence, we recommend an inventive way to compute the sentiments for given reviews or opinions. This recommendation is centered on the social networking sites’ information of various Tweets, a word-emotion-association-network is put up in association to represent opinions and semantics that decides the base for the emotions (sentiment) analysis of opinion or reviews.

Network Text Analysis and Sentiment Analysis

Big Data ◽  
2016 ◽  
pp. 1277-1294
Author(s):  
Veronica Ravaglia ◽  
Luca Zanazzi ◽  
Elvis Mazzoni
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Social Networking Sites ◽  
Word Of Mouth ◽  
Network Text Analysis ◽  
Online Conversations ◽  
Web Forums

Through Social Media, like social networking sites, wikis, web forums or blogs, people can debate and influence each other. Due to this reason, the analysis of online conversations has been recognized to be relevant to organizations. In the chapter we introduce two strategic tools to monitor and analyze online conversations, Sentiment Text Analysis (STA) and Network Text Analysis (NTA). Finally, we propose one empirical example in which these tools are integrated to analyze Word-of-Mouth regarding products and services in the Digital Marketplace.

scholarly journals Sentiment Analysis about Smart Phones Using Twitter Corpus by Deep Learning Approach

2019 ◽  
Vol 8 (S2) ◽  
pp. 39-45
Author(s):  
R. Pavithra ◽  
A. R. Mohamed Shanavas
Keyword(s):  
Neural Networks ◽  
Social Media ◽  
Deep Learning ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Convolutional Neural Networks ◽  
Social Networking Sites ◽  
Smart Phones ◽  
Learning Approach ◽  
Human Beings

Micro blogging websites are nothing but social media website to which user makes quick and frequent posts. Twitter is one of the well-known micro blog sites which offer the space for person which can read and put up messages that are 148 characters in duration. Twitter messages also are referred to as Tweets. And will use these tweets as raw facts. Then use a way that automatically extracts tweets into advantageous, bad or neutral sentiments. By the usage of the sentiment evaluation the consumer can recognize the feedback about the product or services before make a purchase. The organization can use sentiment evaluation to know the opinion of clients about their products, so can examine customer pleasure and in line with that they could improve their product. Now-a-days social networking sites are at the growth, so massive amount of data is generated. Millions of human beings are sharing their views each day on micro blogging sites, since it includes short and simple expressions. In this thesis, able to discuss approximately a paradigm to extract the sentiment from a famous micro running a blog carrier, Twitter, wherein customers submit their opinions for the whole thing. And can use the deep mastering algorithm to categories the twitters which incorporates Convolutional Neural Networks. The experimental end result is presented to demonstrate the use and effectiveness of the proposed system.

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 Predicting Election Results using NLTK

2019 ◽  
Vol 9 (1) ◽  
pp. 4519-4529
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Text Processing ◽  
Research Work ◽  
Supervised Machine Learning ◽  
Analysis Tool ◽  
Machine Learning Classification

In today’s world, people are usually using social media networks for trying to communicate with other users and for sharing information across the world. The online social networking sites have become considerable tools and are providing a common medium for a number of users to communicate with each other. Twitter is the most prominent microblogging website and one among the social networking sites that grow on a daily basis. Social media incorporates an extensive amount of data in the form of tweets, forums, status updates, comments, etc. in an attempt to automatically process and analyze these data, applications can rely on analysis approaches such as sentiment analysis. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), to obtain user's opinions and sentiments. Natural Language Toolkit (NLTK) is a library based on machine learning methods in python & sentiment analysis tool. Which provides the base for text processing and classification? The research work proposed a machine learning-based classifier to extract the tweets on elections and analyze the opinion of the tweeples (people who use twitter). The tweets can be categorized as positive, negative and neutral towards a particular politician. We classify these processed tweets using a supervised machine learning classification approach. The classifier used to classify the tweets as positive, negative or neutral is Naive Bayes Classifier. The classifier is trained with tweets bearing a distinctive polarity. The percentage of positive and negative tweets is then measured and graphically represented.

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.

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