Reprint of “Supervised sentiment analysis in Czech social media”

Habernal, Ivan; Ptáček, Tomáš; Shinar, J.

doi:10.1016/j.ipm.2015.05.006

Cited by 23 publications

(8 citation statements)

References 29 publications

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“…Inspired by this work, other researchers have applied the same strategy when dealing with text sentiment classification. Some of the examples are Pak and Paroubek (2010), Barbosa and Feng (2010), Ye et al (2009), and Habernal et al (2014). However, it is also discussed in literature that when dealing with a corpus, in most of the cases it is not enough to represent documents as word frequency or binary vectors.…”

Section: Samentioning

confidence: 99%

Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter

et al. 2021

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Finance literature in sports focuses on three main methods of stock price prediction in soccer: based on match results, pre-match expectations, or match importance. For pre-match expectations, betting odds is commonly used as the indicator of investors’ sentiments. We propose to include Twitter data as another indicator of this variable, and analyze the links among soccer match results, sentiments, and stock returns of the four major Turkish soccer teams. Our results show that social media can be a strong indicator of pre-match expectations and investors’ sentiments in stock price prediction.

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Section: Samentioning

confidence: 99%

Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter

et al. 2021

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“…Lexicon-based approaches rely on a dictionary of opinion words and classify sentiment as positive or negative (Dhaoui et al, 2017). Machine learning approaches can be conducted in pre-known or unknown categories via fully automated clustering, LDA or computer-assisted clustering (Amplayo & Song, 2017;Colace et al, 2015;Habernal et al, 2015).…”

Section: Sentiment Analysis In Current Researchmentioning

confidence: 99%

“…With the proliferation of social networks and the digitization of research methods, sentiment analysis is increasingly applied to content from social media such as Twitter or Facebook (Amplayo & Song, 2017;Habernal et al, 2015;Xia et al, 2016). Habernal et al's sentiment analysis looked at Facebook posts written in Czech that contained opinions on particular brands.…”

Section: Sentiment Analysis In Current Researchmentioning

confidence: 99%

Spatialities, Social Media and Sentiment Analysis: Exploring the Potential of the Detection Tool SentiStrength

Reithmeier¹,

Buschbaum²,

Kanwischer³

2018

giforum

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Social media such as Twitter or Facebook not only create new spaces of interaction and communication, they also influence the way we perceive things and lead to changes in our self-perception and our own worldview. Online data occur in various forms and can contain opinions or expressions of feeling. In this article, we explore the potential of SentiStrength, a tool for sentiment analysis in geographic research. We analyse posts on Twitter containing hashtags for possible constructions of spaces in Ostend, a neighbourhood in Frankfurt, Germany. We collected tweets via the Twitter API and used the SentiStrength online application to conduct our sentiment analysis. In order to evaluate the results, we also classified our data manually for comparison. Through its lexicon-based classification, the tool was able to identify positive and negative associations of Ostend. However, we were also able to demonstrate the limitations of the tool compared to manual analysis. Although it provides a quick and comprehensive overview of sentiments, SentiStrength reaches its limits when other media such as images are involved. Overall, the tool offers a good low-threshold approach for scientists to work with digital data.

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“…Thus, several works have focused on feature extraction through the N -grams [ 29 ] such as unigrams, bigrams, and trigrams. Other proposals evaluate methods based on term frequency-inverse document frequency (TF-IDF), dependency features [ 33 ], POS-related features [ 34 ], and some cases on their combination [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

Salas-Zárate

Medina-Moreira

Lagos-Ortíz

et al. 2017

Computational and Mathematical Methods in Medicine

117

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In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%.

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Reprint of “Supervised sentiment analysis in Czech social media”

Cited by 23 publications

References 29 publications

Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter

Can Social Media Predict Soccer Clubs’ Stock Prices? The Case of Turkish Teams and Twitter

Spatialities, Social Media and Sentiment Analysis: Exploring the Potential of the Detection Tool SentiStrength

Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

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