Survey on user emotion analysis using Twitter data

Subramaniam, G.; Aswini, R.; Ranjitha, M; Rajendran, Praveen Kumar

doi:10.1109/icecds.2017.8389587

Cited by 7 publications

(3 citation statements)

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“…Twitter, a text-rich social media, is a valuable and popular data source for emotion analysis (Sailunaz and Alhajj, 2019;Colneriĉ and Demsar, 2018;Subramaniam et al, 2017). Previous studies have proposed methods to detect and analyze Twitter users' emotional responses towards an event (Jones et al, 2016) and understand users' mental health (Wang et al, 2016;Seabrook et al, 2018).…”

Section: Related Workmentioning

confidence: 99%

“…Previous studies of emotion analysis on Twitter largely focused on identifying emotions for general tweets (Sailunaz et al, 2018;Colneriĉ and Demsar, 2018;Subramaniam et al, 2017), and during the COVID-19 period (Mukherjee et al, 2020;Gupta et al, 2020). In analyzing emotion dynamics on Twitter, Naskar et al (2019) modelled emotional states of Twitter users with a Hidden Markov Model, and further showed that Twitter users change their emotional state against their topics (Naskar et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

Ng¹,

Lee²,

Awal³

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

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With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter. Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic. After tweets are annotated with dominant emotions and topics, a timeseries emotion analysis is used to identify disgust and anger as the most commonly identified emotions. Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states. Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust → anger, (3) optimism → joy. (4) erratic motion over a prolonged period. Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information. Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

Ng¹,

Lee²,

Awal³

2020

Proceedings of the Fourth Workshop on Natural Language Processing and Computational Social Science

View full text Add to dashboard Cite

show abstract

“…The model works based on the twitter media and interprets the sentimental emotions to update the information on their survey website. M.Trupthi, Suresh Pabboju [4], has given an intuitive programmed framework which predicts the estimation of the survey/tweets of the individuals posted in online networking utilizing hadoop. The system proposed extracted the data from SNS services which is done using Streaming API of twitter.…”

Section: Literature Surveymentioning

confidence: 99%

Sentiment Classification System of Twitter Data using Python

Balaji*,

Khilar

2020

IJRTE

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Abstract: Social media has developed drastically over the years. These days, individuals from all around the globe utilize online networking destinations to share data and information. Twitter is a well known communication site where users update information or messages known as tweets. Users share their day by day lives, post their opinions on everything, for example, brands and places. Various purchasers and advertisers utilize these tweets to accumulate bits of knowledge of their items and opinions on them. The aim of this paper is to exhibit a model that can perform sentiment analysis of real-time data collected from twitter and classify the tweets into positive, negative or neutral based on the sentiment expressed in them.

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Exploration of sentiment analysis in twitter propaganda: a deep dive

Samuel,

Krishna

et al. 2023

Multimed Tools Appl

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Survey on user emotion analysis using Twitter data

Cited by 7 publications

References 0 publications

I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

Sentiment Classification System of Twitter Data using Python

Exploration of sentiment analysis in twitter propaganda: a deep dive

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