Tunisian Dialect Resources for Opinion Analysis on Social Media

2018 ◽  
Author(s):  
Emna Fsih ◽  
Rahma Boujelbane ◽  
Lamia Hadrich Belguith
Keyword(s):  
Social Media ◽  
Opinion Analysis

scholarly journals A WEB-BASED FAST AND RELIABLE TEXT CLASSIFICATION TOOL

2019 ◽  
Vol 1 ◽  
pp. 24
Author(s):  
Jānis Kapenieks
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Storage ◽  
Text Classification ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Web Based ◽  
Opinion Analysis ◽  
Classification Tool ◽  
User Friendly

INTRODUCTION Opinion analysis in the big data analysis context has been a hot topic in science and the business world recently. Social media has become a key data source for opinions generating a large amount of data every day providing content for further analysis. In the Big data age, unstructured data classification is one of the key tools for fast and reliable content analysis. I expect significant growth in the demand for content classification services in the nearest future. There are many online text classification tools available providing limited functionality -such as automated text classification in predefined categories and sentiment analysis based on a pre-trained machine learning algorithm. The limited functionality does not provide tools such as data mining support and/or a machine learning algorithm training interface. There are a limited number of tools available providing the whole sets of tools required for text classification, i.e. this includes all the steps starting from data mining till building a machine learning algorithm and applying it to a data stream from a social network source. My goal is to create a tool able to generate a classified text stream directly from social media with a user friendly set-up interface. METHODS AND MATERIALS The text classification tool will have a core based modular structure (each module providing certain functionality) so the system can be scaled in terms of technology and functionality. The tool will be built on open source libraries and programming languages running on a Linux OS based server. The tool will be based on three key components: frontend, backend and data storage as described below: backend: Python and Nodejs programming language with machine learning and text filtering libraries: TensorFlow, and Keras, for data storage Mysql 5.7/8 will be used, frontend will be based on web technologies built using PHP and Javascript. EXPECTED RESULTS The expected result of my work is a web-based text classification tool for opinion analysis using data streams from social media. The tool will provide a user friendly interface for data collection, algorithm selection, machine learning algorithm setup and training. Multiple text classification algorithms will be available as listed below: Linear SVM Random Forest Multinomial Naive Bayes Bernoulli Naive Bayes Ridge Regressio Perceptron Passive Aggressive Classifier Deep machine learning algorithm. System users will be able to identify the most effective algorithm for their text classification task and compare them based on their accuracy. The architecture of the text classification tool will be based on a frontend interface and backend services. The frontend interface will provide all the tools the system user will be interacting with the system. This includes setting up data collection streams from multiple social networks and allocating them to pre-specified channels based on keywords. Data from each channel can be classified and assigned to a pre-defined cluster. The tool will provide a training interface for machine learning algorithms. This text classification tool is currently in active development for a client with planned testing and implementation in April 2019.

scholarly journals A Comprehensive Analysis of Approaches for Sentiment Analysis Using Twitter Data on COVID-19 Vaccines

2021 ◽  
Vol 2 (2) ◽  
pp. 1-10
Author(s):  
Amrita Mishra ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Text Classification ◽  
Comprehensive Analysis ◽  
Social Media Data ◽  
The Public ◽  
Opinion Analysis ◽  
Twitter Data ◽  
Media Data

Sentiment Analysis has paved routes for opinion analysis of masses over unrestricted territorial limits. With the advent and growth of social media like Twitter, Facebook, WhatsApp, Snapchat in today’s world, stakeholders and the public often takes to expressing their opinion on them and drawing conclusions. While these social media data are extremely informative and well connected, the major challenge lies in incorporating efficient Text Classification strategies which not only overcomes the unstructured and humongous nature of data but also generates correct polarity of opinions (i.e. positive, negative, and neutral). This paper is a thorough effort to provide a brief study about various approaches to SA including Machine Learning, Lexicon Based, and Automatic Approaches. The paper also highlights the comparison of positive, negative, and neutral tweets of the Sputnik V, Moderna, and Covaxin vaccines used for preventive and emergency use of COVID-19 disease.

Geo-Sentiment Analysis as a Location-Based Opinion Analysis System on Public Opinion Data about Governor Candidates

2018 ◽  
Vol 7 (4.44) ◽  
pp. 110
Author(s):  
Imam Fahrur Rozi ◽  
Dika Rizky Yunianto ◽  
Mustika Mentari ◽  
Awan Setiawan ◽  
Rudy Ariyanto ◽  
...  
Keyword(s):  
Social Media ◽  
Text Classification ◽  
Classification System ◽  
Classification Method ◽  
Bayes Classifier ◽  
Opinion Analysis ◽  
Analysis System ◽  
User Friendly ◽  
Pie Chart

Ahead of governor elections, there were a lot of news and opinions related to the candidates through social media. The candidates could map the positive public opinions as their political supports that need to be strengthened, and the negative opinions that need for correction. To map those opinions, it is necessary for an opinion classification system from textual opinions. It became the focus of this research. The system was designed to work on textual opinions in Bahasa since the proposed case study was the opinion of East Java governor candidates mainly written in Bahasa. Classification method that was used to classify the opinions in this system, is Naive Bayes Classifier (NBC). The opinions would be classified into 2 classes, negative and positive opinion. The classified opinions then grouped by region. It would make users easier to map the opinion in each region. The visualization became more user-friendly since the count of classified opinion displayed as a pie chart on a geographical mode or a map. After testing on the classification results, the accuracy value that we got was 78%. It indicated that NBC could perform very well as a simple text classification method with a good result. 

Impact of Credibility on Opinion Analysis in Social Media

2018 ◽  
Vol 162 (4) ◽  
pp. 259-281
Author(s):  
Fatima Zohra Ennaji ◽  
Lobna Azaza ◽  
Zakaria Maamar ◽  
Abdelaziz El Fazziki ◽  
Marinette Savonnet ◽  
...  
Keyword(s):  
Social Media ◽  
Opinion Analysis

scholarly journals Geo-based Social Media Analytics and SMART Dashboard for Tracking Influenza Outbreaks

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Ming-Hsiang Tsou ◽  
Michael Peddecord ◽  
Jeffrey Johnson ◽  
Chin-Te Jung
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Local Level ◽  
Outbreak Detection ◽  
Social Media Analytics ◽  
Influenza Like Illness ◽  
Opinion Analysis ◽  
Influenza Outbreaks ◽  
Us Cities ◽  
Daily Changes

To track outbreaks of influenza (flu), we computed twitter rates from 31 US cities and compare rates to influenza-like-illness (ILI) surveillance rates. Over 2 flu seasons, 2012-14, significant correlations and similar graphic patterns were observed. We demonstrate an interactive dashboard "SMART" that allows practitioners to monitor and visualize daily changes of flu tweets and related news. Compared to regional or national approaches such a GoogleFluTrends, this system allows rapid public opinion analysis and flu outbreak detection at the local level. The SMART dashboard can provide timely and actionable information for local for agencies and practitioners.

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