[PDF] Top 20 NSEmo at EmoInt 2017: An Ensemble to Predict Emotion Intensity in Tweets
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NSEmo at EmoInt 2017: An Ensemble to Predict Emotion Intensity in Tweets
... dict emotion intensity in tweets. Our ap- proach is an ensemble of three regression ...An ensemble of these three meth- ods gives better performance than individ- ual ... See full document
6
GradAscent at EmoInt 2017: Character and Word Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
... WASSA 2017 EmoInt shared task has the goal to predict emotion intensity val- ues of tweet ...its emotion category (anger, joy, fear, and sadness), the participants were asked to ... See full document
6
NUIG at EmoInt 2017: BiLSTM and SVR Ensemble to Detect Emotion Intensity
... WASSA 2017 shared task on emotion in- tensity (EmoInt) is a competition intended to stimulate research into emotion recognition from text (Mohammad and Bravo-Marquez, ...language tweets ... See full document
5
IMS at EmoInt 2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning
... Our submission to the WASSA-2017 shared task on the prediction of emotion intensity in tweets is a supervised learning method with extended lexicons of affective norms. We combine three main ... See full document
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UWat Emote at EmoInt 2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings
... for EmoInt- ...affect intensity, af- fect presence, sentiment intensity and sentiment presence lexica alongside pre- trained word embeddings, which are uti- lized to extract emotion ... See full document
6
WASSA 2017 Shared Task on Emotion Intensity
... The best performing system, Prayas, used an en- semble of three different models: The first is a feed-forward neural network whose input vector is formed by concatenating the average word embed- ding vector with the ... See full document
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Seernet at EmoInt 2017: Tweet Emotion Intensity Estimator
... The relative feature importance can be assessed by the relative depth of the feature used as a de- cision node in the tree. Features used at the top of the tree contribute to the final prediction deci- sion of a larger ... See full document
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YNU HPCC at EmoInt 2017: Using a CNN LSTM Model for Sentiment Intensity Prediction
... an emotion (anger, fear, joy and sad- ...contain tweets along with a real-valued score (between 0 and 1) indi- cating the degree of the emotion felt by the speak- ...process tweets into an ... See full document
5
IITP at EmoInt 2017: Measuring Intensity of Emotions using Sentence Embeddings and Optimized Features
... We then cascade the LSTM network with SVR for the final predictions (LSTM+SVR). On cascad- ing we obtain 0.6641 average Pearson score, re- porting a gain of 0.04 points. Finally, to further improve the prediction ... See full document
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Prayas at EmoInt 2017: An Ensemble of Deep Neural Architectures for Emotion Intensity Prediction in Tweets
... to predict the intensity of the emotion in tweets exhibiting that ...an ensemble of Feed-Forward Neural Networks, Multi-Task Deep Learning and Sequence Modeling using CNNs and LSTMs, ... See full document
8
DMGroup at EmoInt 2017: Emotion Intensity Using Ensemble Method
... analyzing emotion have been ...to predict sentiments in text, the approach showed that SVM classifiers trained using bag-of- words features produced hopeful ...used emotion icons in the blog posts as ... See full document
6
Exploring Fine Grained Emotion Detection in Tweets
... We ran a series of experiments to identify a set of machine learning algorithms that generally per- form well for this task. Four machine learning al- gorithms were found to perform well in this prob- lem space: support ... See full document
8
Can Tweets Predict TV Ratings?
... of tweets posted during all episodes of all pro- grammes, as well as the best-fitting linear regression ...of tweets and the TV ratings for these ... See full document
6
Bootstrapped Learning of Emotion Hashtags #hashtags4you
... many tweets in our test data have at least one of our 5 emotions, we manually selected 25 topic keywords/phrases 6 that we considered to be strongly associated with emotions, but not neces- sarily any specific ... See full document
10
Predicting the Emotional Intensity of Tweets
... The language features of the texts used to train deep learning models may also play an important role. We did not use any word embeddings that were trained on Twitter data. Other results sug- gest that embeddings trained ... See full document
111
Tecnolengua Lingmotif at EmoInt 2017: A lexicon based approach
... In this paper we describe Tecnolengua Group’s participation in the shared task on emotion intensity at WASSA 2017. We used the Lingmotif tool and a new, com- plementary tool, Lingmotif Learn, which ... See full document
8
Feature Enriched Character Level Convolutions for Text Regression
... of Emotion Intensity Analysis (EIA), Neural Networks have not yet been successfully ...certain emotion in a text, such as fear, anger, joy, sadness, ...tion Intensity shared task of SemEval ... See full document
7
Revisiting the synoptic-scale predictability of severe European winter storms using ECMWF ensemble reforecasts
... the ensemble average has small biases in terms of predicting the position and minimum MSLP of the storms on the day of maximum ...the intensity forecasts of severe storms only (Pirret et ...The ... See full document
16
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories
... In closing her landmark paper on affective com- puting, Rosalind Picard charges researchers of artificial intelligence with a task. She writes, “Computers that will interact naturally and intelligently with humans need ... See full document
5
Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases
... 2012), emotion influ- encing behaviors (Kim et ...hierarchical emotion classifica- tion (Ghazi et ...of tweets, but pri- marily to collect labeled data ... See full document
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