scholarly journals Lexicon based Acronyms and Emoticons Classification of Sentiment Analysis (SA) on Big Data

2017 ◽  
Vol 10 (7) ◽  
pp. 41-54
Author(s):  
M. Edison ◽  
A. Aloysius
Keyword(s):  
Big Data ◽  
Sentiment Analysis

scholarly journals O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Fabio Malini ◽  
Patrick Ciarelli ◽  
Jean Medeiros
Keyword(s):  
Social Networks ◽  
Genetic Algorithms ◽  
Big Data ◽  
Sentiment Analysis ◽  
Text Analysis ◽  
Negative Polarization ◽  
Political Networks ◽  
Networks Analysis ◽  
Fear And Anxiety

Resumo Este artigo se propõe a ampliar a metodologia perspectivista (MALINI, 2016) de análise de redes sociais, incorporando um procedimento de análise dos sentimentos das mensagens postadas em redes de controvérsias políticas, em particular, em dois momentos distintos da campanha pelo impeachment da presidenta Dilma. O primeiro é o período da eclosão das manifestações antipetistas, no dia 15 de março de 2015. O segundo, dia 27 de agosto de 2016, quando a presidenta é deposta do cargo. Realiza uma revisão sobre a análise de sentimentos em megadados do Twitter e constrói uma metodologia que combina classificação humana de textos com aplicação de algoritmos genéticos de análise de textos, no intuito de analisar sentimentos genéricos (baseado na polarização positivo/negativos) e sentimento específicos, baseados nas seguintes emoções: Alegria, Raiva, Medo, Antecipação, Desgosto, Tristeza, Surpresa e Confiança. Conclui demonstrando que os movimentos pró e anti-Dilma são marcados pelo predomínio de sentimento de raiva, medo e ansiedade, confirmando a hipótese que a trolagem ofensiva demarca o estilo da indignação propagada em redes políticas no Twitter brasileiro.  Palavras-Chave: Análise de Sentimento; Big Data; Redes; Política; Twitter.Abstract This article aims to expand the perspectivist methodology (Malini, 2016) of social networks analysis, incorporating a proceeding of sentiment analysis of the messages posted in networks of political controversies, in particular, in two distinct moments of the campaign for the impeachment of President Dilma. The first is the period of the outbreak of PT protests, on March 15, 2015. The second, on August 27, 2016, when the president is deposed. We will be doing a theoretical review about sentiment analysis in Big Data on Twitter to build a methodology that combines human classification of texts with the application of genetic algorithms of text analysis and to analyze generic sentiments (based on positive / negative polarization) and specific sentiment, based on emotions like Joy, Anger, Fear, Anticipation, Disgust, Sadness, Surprise and Trust. It concludes by demonstrating that pro and anti-Dilma movements are marked by a predominance of anger, fear and anxiety, confirming the hypothesis that an offensive trolling demarcates the style of indignation propagated by political networks in Brazilian Twitter.Keywords: Sentiment Analysis; Big Data; Social Network; Politics; Twitter. 

Sentiment Analysis of Twitter Data

2019 ◽  
Vol 14 (2) ◽  
pp. 1-16 ◽  
Author(s):  
Ankit Srivastava ◽  
Vijendra Singh ◽  
Gurdeep Singh Drall
Keyword(s):  
Big Data ◽  
Sentiment Analysis ◽  
Naive Bayes ◽  
Hybrid Approach ◽  
Naïve Bayes ◽  
Data Sets ◽  
The Novel ◽  
New Techniques ◽  
Hidden Knowledge

Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.

scholarly journals Sentiment Trend Analysis of Big Data

2019 ◽  
Vol 8 (10) ◽  
pp. 59-64
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Real Time ◽  
Language Processing ◽  
Opinion Mining ◽  
Data Sources ◽  
Product Reviews

Various fields like Text Mining, Linguistics, Decision Making and Natural Language Processing together form the basis for Opinion Mining or Sentiment Analysis. People share their feelings, observations and thoughts on social media, which has emerged as a powerful tool for rapidly growing enormous repository of real time discussions and thoughts shared by people. In this paper, we aim to decipher the current popular opinions or emotions from various sources, hence, contributing to sentiment analysis domain. Text from social media, blogs and product reviews are classified according to the sentiment they project. We re-examine the traditional processes of sentiment extraction, to incorporate the increase in complexity and number of the data sources and relevant topics, while re-populating the meaning of sentiment. Working across and within numerous streams of social media, expression of sentiment and classification of polarity is re-examined, thereby redefining and enhancing the realm of sentiment. Numerous social media streams are analyzed to build datasets that are topical for each stream and are later polarized according to their sentiment expression. In conclusion, defining a sentiment and developing tools for its analysis in real time of human idea exchange is the motive.

scholarly journals Enhanced concept-level sentiment analysis system with expanded ontological relations for efficient classification of user reviews

2021 ◽  
Author(s):  
Asad Khattak ◽  
Muhammad Zubair Asghar ◽  
Zain Ishaq ◽  
Waqas Haider Bangyal ◽  
Ibrahim A Hameed
Keyword(s):  
Sentiment Analysis ◽  
User Reviews ◽  
Analysis System

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

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.

scholarly journals Overview and Future Opportunities of Sentiment Analysis Approaches for Big Data

2016 ◽  
Vol 12 (3) ◽  
pp. 153-168 ◽  
Author(s):  
Nurfadhlina Mohd Sharef ◽  
Harnani Mat Zin ◽  
Samaneh Nadali
Keyword(s):  
Big Data ◽  
Sentiment Analysis

Sentiment Analysis with Text Mining in Contexts of Big Data

2017 ◽  
Vol 13 (3) ◽  
pp. 47-67 ◽  
Author(s):  
Carina Sofia Andrade ◽  
Maribel Yasmina Santos
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Decision Making Process ◽  
Practical Applications ◽  
Information Models ◽  
Evolution Of Technology ◽  
The Common ◽  
Analyse Data

The evolution of technology, along with the common use of different devices connected to the Internet, provides a vast growth in the volume and variety of data that are daily generated at high velocity, phenomenon commonly denominated as Big Data. Related with this, several Text Mining techniques make possible the extraction of useful insights from that data, benefiting the decision-making process across multiple areas, using the information, models, patterns or tendencies that these techniques are able to identify. With Sentiment Analysis, it is possible to understand which sentiments and opinions are implicit in this data. This paper proposes an architecture for Sentiment Analysis that uses data from the Twitter, which is able to collect, store, process and analyse data on a real-time fashion. To demonstrate its utility, practical applications are developed using real world examples where Sentiment Analysis brings benefits when applied. With the presented demonstration case, it is possible to verify the role of each used technology and the techniques adopted for Sentiment Analysis.

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