UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Přibáň, Pavel; Hercig, Tomáš; Lenc, Ladislav

doi:10.18653/v1/s18-1018

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…In the literature wide range of features have been explored in the task of tweet sentiment analysis including unigrams, bigrams, n-grams, part-of-speech (POS) tags, word embedding, word clusters [23], [24], [25], [26], [7], [27]. In this work we use TweetToSparseFeatureVector filter in Weka Affective tweets [4] package to extract word n-grams, character n-grams, brown word clusters and part-ofspeech tags.…”

Section: Tweet Feature Extractionmentioning

confidence: 99%

Emotion Mining in Social Media Data

Ranganathan

Tzacheva

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

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Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today's world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer's emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users's emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user's can enhance their feeling for a better healthy lifestyle we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user's.

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Section: Tweet Feature Extractionmentioning

confidence: 99%

Emotion Mining in Social Media Data

Ranganathan

Tzacheva

2019

Computer Science &Amp; Information Technology (CS &Amp; IT )

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“…Tweets often contain slang expressions, misspelled words, emoticons or abbreviations and it is needed to make some preprocessing steps before training and making predictions. We use a similar approach to Přibáň et al (2018).…”

Section: Tweets Preprocessingmentioning

confidence: 99%

UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network

Přibáň

Martínek²

2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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This paper describes our system created for the WASSA 2018 Implicit Emotion Shared Task. The goal of this task is to predict the emotion of a given tweet, from which a certain emotion word is removed. The removed word can be sad, happy, disgusted, angry, afraid or a synonym of one of them. Our proposed system is based on deep-learning methods. We use Bidirectional Long Short-Term Memory (BiLSTM) with word embeddings as an input. Pre-trained DeepMoji model and pre-trained emoji2vec emoji embeddings are also used as additional inputs. Our System achieves 0.657 macro F 1 score and our rank is 13 th out of 30.

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UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets

Cited by 2 publications

References 23 publications

Emotion Mining in Social Media Data

Emotion Mining in Social Media Data

UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network

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