Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment

Barbosa, Geraldo A.; Silva, Ismael S.; Zaki, Mohammed J.; Meira, Wagner; Prates, Raquel O.; Veloso, Adriano

doi:10.1145/2212776.2223846

Cited by 10 publications

(3 citation statements)

References 9 publications

(9 reference statements)

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“…Rodrigues Barbosa et al [ 29 ] used text mining processes to explore tweets that spoke about the Brazilian presidential elections in 2010 to trace the online sentiment of the population expressed in tweets, classifying them into positive, negative, and neutral, and to correlate the ranking of tweets to the events occurring in Brazil at the time of the elections, such as political debates, for example. Rodrigues Barbosa et al [ 29 ] pointed out that Twitter’s interaction model induces users to continually share and express their opinions and feelings, which are propagated to their followers. However, determining the sentiment each tweet expresses can be a laborious task, prone to errors and ambiguity.…”

Section: State-of-the-art Review and Related Workmentioning

confidence: 99%

“…However, determining the sentiment each tweet expresses can be a laborious task, prone to errors and ambiguity. Getting around these challenges, the work in [ 29 ] explored hashtags. In this particular case, hashtags were used to determine the sentiment expressed by Twitter users in the tweets referring to the Brazilian presidential election in 2010.…”

Section: State-of-the-art Review and Related Workmentioning

confidence: 99%

“…This paper proposes functions analogous to those presented in [29,30,[32][33][34] to determine the sentiment expressed by users, correlating the results to the facts that occurred in a certain period focused on a context of interest. However, it differs from the aforementioned works due to its operation being executed in real time, besides considering other words of a tweet that may express a sentiment, even if they are not marked with a hashtag.…”

Section: Main Contribution Of This Workmentioning

confidence: 99%

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Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

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Section: State-of-the-art Review and Related Workmentioning

confidence: 99%

Section: State-of-the-art Review and Related Workmentioning

confidence: 99%

Section: Main Contribution Of This Workmentioning

confidence: 99%

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A Deep Learning Approach for Sentiment Analysis in Spanish Tweets

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Detecting sociosemantic communities by applying social network analysis in tweets

Abascal-Mena

Lema

Sèdes

2015

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International audienceVirtual social networks have led to a new way of communication that is different from the oral one, where the restriction of time and space generates new linguistic practices. Twitter, a medium for political and social discussion, can be analyzed to understand new ways of communication and to explore sociosemiotics aspects that come with the use of the hashtags and their relationship with other elements. This paper presents a quantitative study of tweets, around a fixed hashtag, in relation with other contents that users bring to connection. By calculating the frequency of terms, a table of nodes and edges is created to visualize tweets like graphs. Our study applies social network analysis that, going beyond mere topology, reveals relevant sociosemantic communities providing insights for the comparison of social and political movements

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Characterizing the effectiveness of twitter hashtags to detect and track online population sentiment

Cited by 10 publications

References 9 publications

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

A Deep Learning Approach for Sentiment Analysis in Spanish Tweets

Detecting sociosemantic communities by applying social network analysis in tweets

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