Using VADER sentiment and SVM for predicting customer response sentiment

Borg, Anton; Boldt, Martin

doi:10.1016/j.eswa.2020.113746

Cited by 138 publications

(49 citation statements)

References 14 publications

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“…In addition to being freely available, VADER has attained certain popularity within the academic community. For instance, researchers have used VADER to determine the sentiment of students toward teachers (Newman and Joyner, 2018 ), to find sentiments toward bitcoin and cryptocurrency on Twitter (Cavalli and Amoretti, 2021 ), and to extract sentiments from e-mails (Borg and Boldt, 2020 ), and from Amazon reviews (Dey et al, 2018 ).…”

Section: Methods and Proceduresmentioning

confidence: 99%

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

et al. 2021

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The COVID-19 pandemic hit hard society, strongly affecting the emotions of the people and wellbeing. It is difficult to measure how the pandemic has affected the sentiment of the people, not to mention how people responded to the dramatic events that took place during the pandemic. This study contributes to this discussion by showing that the negative perception of the people of the COVID-19 pandemic is dropping. By negative perception, we mean the number of negative words the users of Twitter, a social media platform, employ in their online posts. Seen as aggregate, Twitter users are using less and less negative words as the pandemic evolves. The conclusion that the negative perception is dropping comes from a careful analysis we made in the contents of the COVID-19 Twitter chatter dataset, a comprehensive database accounting for more than 1 billion posts generated during the pandemic. We explore why the negativity of the people decreases, making connections with psychological traits such as psychophysical numbing, reappraisal, suppression, and resilience. In particular, we show that the negative perception decreased intensively when the vaccination campaign started in the USA, Canada, and the UK and has remained to decrease steadily since then. This finding led us to conclude that vaccination plays a key role in dropping the negativity of the people, thus promoting their psychological wellbeing.

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Section: Methods and Proceduresmentioning

confidence: 99%

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

et al. 2021

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“…Here polarity score is of type float and ranges between [−1.0 and +1.0]. VADER takes into consideration emojis, slangs, emoticons, degree modifiers and capitalizations for score calculation (Hutto and Gilbert, 2014; Becken et al , 2019; Borg and Boldt, 2020; Moutidis and Williams, 2020). Only Tweets in the English language were captured.…”

Section: Methodsmentioning

confidence: 99%

Twitter users’ coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods

Mittal

Ahmed

Mittal

et al. 2021

IDD

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Purpose Using data from Twitter, the purpose of this paper is to assess the coping behaviour and reactions of social media users in response to the initial days of the COVID-19-related lockdown in different parts of the world. Design/methodology/approach This study follows the quasi-inductive approach which allows the development of pre-categories from other theories before the sampling and coding processes begin, for use in those processes. Data was extracted using relevant keywords from Twitter, and a sample was drawn from the Twitter data set to ensure the data is more manageable from a qualitative research standpoint and that meaningful interpretations can be drawn from the data analysis results. The data analysis is discussed in two parts: extraction and classification of data from Twitter using automated sentiment analysis; and qualitative data analysis of a smaller Twitter data sample. Findings This study found that during the lockdown the majority of users on Twitter shared positive opinions towards the lockdown. The results also found that people are keeping themselves engaged and entertained. Governments around the world have also gained support from Twitter users. This is despite the hardships being faced by citizens. The authors also found a number of users expressing negative sentiments. The results also found that several users on Twitter were fence-sitters and their opinions and emotions could swing either way depending on how the pandemic progresses and what action is taken by governments around the world. Research limitations/implications The authors add to the body of literature that has examined Twitter discussions around H1N1 using in-depth qualitative methods and conspiracy theories around COVID-19. In the long run, the government can help citizens develop routines that help the community adapt to a new dangerous environment – this has very effectively been shown in the context of wildfires in the context of disaster management. In the context of this research, the dominance of the positive themes within tweets is promising for policymakers and governments around the world. However, sentiments may wish to be monitored going forward as large-spikes in negative sentiment may highlight lockdown-fatigue. Social implications The psychology of humans during a pandemic can have a profound impact on how COVID-19 shapes up, and this shall also include how people behave with other people and with the larger environment. Lockdowns are the opposite of what societies strive to achieve, i.e. socializing. Originality/value This study is based on original Twitter data collected during the initial days of the COVID-19-induced lockdown. The topic of “lockdowns” and the “COVID-19” pandemic have not been studied together thus far. This study is highly topical.

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“…The fact that VADER is a pre-trained model gives it an advantage with respect to users. For example, Borg et al [37] examine sentiment analysis among customers of a large Swedish telecommunications company. The dataset consists of 168010 emails with no sentiment information available.…”

Section: B Valence Aware Dictionary and Sentiment Reasoner Lexicon And Rule-based Sentiment Analysismentioning

confidence: 99%

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Chiny¹,

Chihab²,

Bencharef³

et al. 2021

IJACSA

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Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rulebased sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and F1 score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.

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Using VADER sentiment and SVM for predicting customer response sentiment

Cited by 138 publications

References 14 publications

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

Twitter users’ coping behaviors during the COVID-19 lockdown: an analysis of tweets using mixed methods

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

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