scholarly journals Multilingual twitter sentiment analysis using machine learning

2020 ◽  
Vol 10 (6) ◽  
pp. 5992
Author(s):  
K. Arun ◽  
A. Srinagesh
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Good Accuracy ◽  
Processing Method ◽  
Social Networking Site ◽  
Data Sparsity ◽  
Research Fields ◽  
Twitter Data ◽  
The World

Twitter sentiment analysis is one of the leading research fields. Most of the researchers were contributed to twitter sentiment analysis in English tweets, but few researchers focus on the multilingual twitter sentiment analysis. Some challenges are hoping for the research solutions in multilingual twitter sentiment analysis. This study presents the implementation of sentiment analysis in multilingual twitter data and improves the data classification up to the adequate level of accuracy. Twitter is the sixth leading social networking site in the world. Active users for twitter in a month are 330 million. People can tweet or re-tweet in their languages and allow users to use emoji’s, abbreviations, contraction words, miss spellings, and shortcut words. The best platform for sentiment analysis is twitter. Multilingual tweets and data sparsity are the two main challenges. In this paper, the MLTSA algorithm gives the solution for these two challenges. MLTSA algorithm divides into two parts. One is detecting and translating non-English tweets into English using natural language processing (NLP). And the second one is an appropriate pre-processing method with NLP support can reduce the data sparsity. The result of the MLTSA with SVM achieves good accuracy by up to 95%.

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 Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

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.

scholarly journals Sentiment Analysis of Patients' Opinions in Healthcare using Lexicon-based Method

2019 ◽  
Vol 9 (1) ◽  
pp. 6977-6981
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Healthcare Service ◽  
Opinion Mining ◽  
Research Area ◽  
Service Needs ◽  
Health Organizations ◽  
The World ◽  
New Research

The rapid increase in technology made people across the world use social networking sites to express their opinions on a topic, product or service. The success of a healthcare service directly depends on its users. If a majority of users like the service then it is a success otherwise, the service needs to be improvised. For improvising the service, the users' opinions need to be analyzed. Manually extracting and analyzing the content present on the web is a tedious task. This gave rise to a new research area called Sentiment Analysis. It is otherwise known as opinion mining. It is being used by many health organizations to make effective decisions on their service. This paper presents the sentiment analysis of patients' opinions on hospitals which is mainly used to improve healthcare service. This is implemented using a lexicon-based methodology to analyze the sentiment.

scholarly journals Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery

2020 ◽  
Author(s):  
Habiba H. Drias ◽  
Yassine Drias
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Association Rules ◽  
Great Majority ◽  
The Other ◽  
Frequent Patterns ◽  
Twitter Data ◽  
Other Hand ◽  
The World

A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.

scholarly journals Sentiment Analysis on Twitter Data: A Comparative Approach

2021 ◽  
Vol 9 (10) ◽  
pp. 1-12
Author(s):  
Subhadip Chandra ◽  
Randrita Sarkar ◽  
Sayon Islam ◽  
Soham Nandi ◽  
Avishto Banerjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Comparative Approach ◽  
Support Vector ◽  
Affective States ◽  
Twitter Data ◽  
Social Media Platforms

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.

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).

Analysis Report for Statistics in the Twitter Network

2021 ◽  
pp. 50-58
Author(s):  
Parvathi R. ◽  
Yamani Sai Asish ◽  
Pattabiraman V.
Keyword(s):  
Social Networking ◽  
Sentiment Analysis ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Direct Connection ◽  
Social Networking Service ◽  
Data Set ◽  
Deep Dive ◽  
The World ◽  
General Opinion

Twitter is the most popular social networking service across the world. In Twitter, the messages are known as tweets. Tweets are mainly text-based posts that can be up to 140 characters long which can reach the author's subscribers. These subscribers are also known as followers. Such subscriptions form a direct connection. But these connections are not always symmetric. In this study, the authors have assumed that if two nodes are connected, then the tweet is propagated between them without any other conditions. But using sentiment analysis, the general opinion of people about various things can be figured. The Twitter data set analyzed includes almost 20k nodes and 33k edges, where the visualization is done with software called Gephi. Later a deep dive analysis is done by calculating some of the metrics such as degree centrality and closeness centrality for the obtained Twitter network. Using this analysis, it is easy to find the influencers in the Twitter network and also the various groups involved in the network.

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