A Comparison of SVM Versus Naive-Bayes Techniques for Sentiment Analysis in Tweets

Alves, André Luiz Firmino; Baptista, Cláudio de Souza; Firmino, Anderson Almeida; Oliveira, Maxwell Guimarães de; Paiva, Anselmo Cardoso de

doi:10.1145/2664551.2664561

Cited by 39 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Para este trabalho, a versão Multinomial do Naive Bayes foi utilizada. O SVM é um classificador binário muito utilizado na tarefa de classificação de textos, principalmente, pelo seu bom desempenho [Firmino Alves et al 2014]. Este classificador busca traçar um hiperplano entre dados de duas classes, buscando maximizar a distância entre os pontos mais próximos em relação a cada uma das classes.…”

Section: Algoritmos De Aprendizado Supervisionadounclassified

A Sentiment Analysis of Brazilian Elections Tweets

Cristiani

Lieira

Camargo

2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

View full text Add to dashboard Cite

The internet connection is present in people’s lives all the time, through smartphones, tablets, computers, among others. The use of social networks is increasingly common around the world. Many companies and people use them to spread products and services and publish opinions, facts that have turned the social networks into powerful sources of information on various topics. Identifying these feelings is a great strategy for many types of decision making. Thus, the purpose of this paper is to collect messages from a specific social network, in this case Twitter, referring to the 2018 Brazilian presidential elections and classify them as: positive, negative and neutral, in order to discover a possible relationship between opinions of social network users and the final outcome of the elections. For this, a corpus was built, preprocessed and evaluated by two different machine learning approaches: Naive Bayes and SVM (Support Vector Machine). The results showed that this social network is a good source of information to perform sentiment analysis and that the number of tweets classified as positive have a strong relationship with the researchers and the final result of the 2018 elections.

show abstract

Section: Algoritmos De Aprendizado Supervisionadounclassified

A Sentiment Analysis of Brazilian Elections Tweets

Cristiani

Lieira

Camargo

2020

Anais Do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2020)

View full text Add to dashboard Cite

show abstract

“…Machine learning methods can be split into two subcategories: (1) conventional methods and (2) deep learning-based [ 14 ]. Conventional approaches apply to traditional machine learning techniques such as Naive Bayes [ 15 ] and support vector machines (SVM) [ 16 ]. Deep learning techniques infer better performance than classical machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

Sadiq

Ahn

Choi

2020

Sensors

View full text Add to dashboard Cite

A rapidly increasing growth of social networks and the propensity of users to communicate their physical activities, thoughts, expressions, and viewpoints in text, visual, and audio material have opened up new possibilities and opportunities in sentiment and activity analysis. Although sentiment and activity analysis of text streams has been extensively studied in the literature, it is relatively recent yet challenging to evaluate sentiment and physical activities together from visuals such as photographs and videos. This paper emphasizes human sentiment in a socially crucial field, namely social media disaster/catastrophe analysis, with associated physical activity analysis. We suggest multi-tagging sentiment and associated activity analyzer fused with a a deep human count tracker, a pragmatic technique for multiple object tracking, and count in occluded circumstances with a reduced number of identity switches in disaster-related videos and images. A crowd-sourcing study has been conducted to analyze and annotate human activity and sentiments towards natural disasters and related images in social networks. The crowdsourcing study outcome into a large-scale benchmark dataset with three annotations sets each resolves distinct tasks. The presented analysis and dataset will anchor a baseline for future research in the domain. We believe that the proposed system will contribute to more viable communities by benefiting different stakeholders, such as news broadcasters, emergency relief organizations, and the public in general.

show abstract

“…Their classifier is able to recognize positive, negative and neutral content. [Firmino Alves et al 2014] proposes an accuracy comparison between two machine learning approaches applied to sentiment analysis: Support Vector Machine and Naïve Bayes. The general idea of this work was to replace POS Tagger for identification of opinionated tweets.…”

Section: Related Workmentioning

confidence: 99%

2014 FIFA World Cup: An Initial Analysis of Collective Sentiments in Twitter

Filho

Magalhães

Silva

et al. 2015

Anais Do Brazilian Workshop on Social Network Analysis and Mining (BraSNAM)

View full text Add to dashboard Cite

Social analysis in Twitter has become a very interesting research scenario. Twitter's popularity and the millions of spontaneous interactions between its users turned the social network into a rich data source. A possible use of this data is to measure collective sentiments related to events. In this short paper, we propose an analysis of the content shared in Twitter during the event 2014 FIFA World Cup Brazil TM. The sentiment classifier utilized in this work is the SentiStrength tool. The steps of this works are Composing the corpus, Preprocessing, Classification and Temporal Analysis. Finally, we propose some preliminary results that indicates the distribution of sentiments across the corpus.

show abstract

A Comparison of SVM Versus Naive-Bayes Techniques for Sentiment Analysis in Tweets

Cited by 39 publications

References 23 publications

A Sentiment Analysis of Brazilian Elections Tweets

A Sentiment Analysis of Brazilian Elections Tweets

Human Sentiment and Activity Recognition in Disaster Situations Using Social Media Images Based on Deep Learning

2014 FIFA World Cup: An Initial Analysis of Collective Sentiments in Twitter

Contact Info

Product

Resources

About