A Prototype Model for Deriving Social Media Intelligence Using Opinion Mining from Microblog Data

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

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Social media and sentiment in bioenergy consultation

International Journal of Energy Sector Management ◽

10.1108/ijesm-11-2014-0007 ◽

2016 ◽

Vol 10 (1) ◽

pp. 87-98 ◽

Cited By ~ 1

Author(s):

Victoria Uren ◽

Daniel Wright ◽

James Scott ◽

Yulan He ◽

Hassan Saif

Keyword(s):

Social Media ◽

New Media ◽

Sentiment Analysis ◽

Opinion Mining ◽

Public Consultation ◽

Short Paper ◽

Consultation Process ◽

Content Type ◽

The Public ◽

The Uk

Purpose – This paper aims to address the following challenge: the push to widen participation in public consultation suggests social media as an additional mechanism through which to engage the public. Bioenergy companies need to build their capacity to communicate in these new media and to monitor the attitudes of the public and opposition organizations towards energy development projects. Design/methodology/approach – This short paper outlines the planning issues bioenergy developments face and the main methods of communication used in the public consultation process in the UK. The potential role of social media in communication with stakeholders is identified. The capacity of sentiment analysis to mine opinions from social media is summarised and illustrated using a sample of tweets containing the term “bioenergy”. Findings – Social media have the potential to improve information flows between stakeholders and developers. Sentiment analysis is a viable methodology, which bioenergy companies should be using to measure public opinion in the consultation process. Preliminary analysis shows promising results. Research limitations/implications – Analysis is preliminary and based on a small dataset. It is intended only to illustrate the potential of sentiment analysis and not to draw general conclusions about the bioenergy sector. Social implications – Social media have the potential to open access to the consultation process and help bioenergy companies to make use of waste for energy developments. Originality/value – Opinion mining, though established in marketing and political analysis, is not yet systematically applied as a planning consultation tool. This is a missed opportunity.

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Temporal sentiment analysis for opinion mining of ASEAN free trade area on social media

2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC) ◽

10.1109/kcic.2016.7883648 ◽

2016 ◽

Cited By ~ 5

Author(s):

Mustofa Kamal ◽

Ali Ridho Barakbah ◽

Nur Rosyid Mubtadai

Keyword(s):

Social Media ◽

Sentiment Analysis ◽

Free Trade ◽

Opinion Mining ◽

Free Trade Area ◽

Trade Area

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Is social media intelligence private? Privacy in public and the nature of social media intelligence

Intelligence on the Frontier Between State and Civil Society ◽

10.1201/9781003009771-5 ◽

2020 ◽

pp. 52-68

Author(s):

Kira Vrist Rønn ◽

Sille Obelitz Søe

Keyword(s):

Social Media ◽

Social Media Intelligence

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SENTIMEN ANALISIS KEBIJAKAN GANJIL GENAP DI TOL BEKASI MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN OPTIMALISASI INFORMATION GAIN

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.705 ◽

2019 ◽

Vol 15 (2) ◽

pp. 247-254

Author(s):

Heru Sukma Utama ◽

Didi Rosiyadi ◽

Dedi Aridarma ◽

Bobby Suryo Prakoso

Keyword(s):

Social Media ◽

Opinion Mining ◽

Naive Bayes ◽

Information Gain ◽

Confusion Matrix ◽

Naïve Bayes ◽

Support Vector ◽

Toll Road ◽

Textual Data ◽

Bayes Algorithm

Analysis of the odd even-numbered sentiment systems in Bekasi toll using the Naïve Bayes Algorithm, is a process of understanding, extracting, and processing textual data automatically from social media. The purpose of this study was to determine the level of accuracy, recall and precision of opinion mining generated using the Naïve Bayes algorithm to provide information community sentiment towards the effectiveness of the odd system of Bekasi tiolls on social media. The research method used in this study was to do text mining in comments-comments regarding posts regarding even odd oddities on Bekasi toll on Twitter, Instagram, Youtube and Facebook. The steps taken are starting from preprocessing, transformation, datamining and evaluation, followed by information gaon feature selection, select by weight and applying NB Algorithm model. The results obtained from the study using the NB model are obtained Confusion Matrix result, namely accuracy of 79,55%, Precision of 80,51%, and Sensitivity or Recall of 80,91%. Thus this study concludes that the use of Support Vector Machine Algorithms can analyze even odd sentiments on the Bekasi toll road.

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