| { | |
| "paper_id": "S18-1018", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T15:44:03.653716Z" | |
| }, | |
| "title": "UWB at SemEval-2018 Task 1: Emotion Intensity Detection in Tweets", | |
| "authors": [ | |
| { | |
| "first": "Pavel", | |
| "middle": [], | |
| "last": "P\u0159ib\u00e1\u0148", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "NTIS -New Technologies for the Information Society", | |
| "institution": "University of West Bohemia", | |
| "location": { | |
| "country": "Czech Republic" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Tom\u00e1\u0161", | |
| "middle": [], | |
| "last": "Hercig", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "NTIS -New Technologies for the Information Society", | |
| "institution": "University of West Bohemia", | |
| "location": { | |
| "country": "Czech Republic" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Ladislav", | |
| "middle": [], | |
| "last": "Lenc", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "NTIS -New Technologies for the Information Society", | |
| "institution": "University of West Bohemia", | |
| "location": { | |
| "country": "Czech Republic" | |
| } | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the Affecti-veTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.", | |
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| "paper_id": "S18-1018", | |
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| "abstract": [ | |
| { | |
| "text": "This paper describes our system created for the SemEval-2018 Task 1: Affect in Tweets (AIT-2018). We participated in both the regression and the ordinal classification subtasks for emotion intensity detection in English, Arabic, and Spanish. For the regression subtask we use the Affecti-veTweets system with added features using various word embeddings, lexicons, and LDA. For the ordinal classification we additionally use our Brainy system with features using parse tree, POS tags, and morphological features. The most beneficial features apart from word and character n-grams include word embeddings, POS count and morphological features.", | |
| "cite_spans": [], | |
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| "section": "Abstract", | |
| "sec_num": null | |
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| "body_text": [ | |
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| "text": "The task of Detecting Emotion Intensity assigns the intensity to a tweet with given emotion. The emotions include anger, fear, joy, and sadness. The intensity is either on a scale of zero to one for the regression subtask, or one of four classes (0:no, 1: low, 2: moderate, 3: high) for the classification subtask. The task was prepared in three languages: English, Arabic, and Spanish. For each language there are four training and test sets of data -one for each emotion. The data creation is described in and detailed description of the task is in .", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "We participated in the emotion intensity regression task (EI-reg) and in the emotion intensity ordinal classification task (EI-oc) in English, Arabic and Spanish.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "We used two separate systems for ordinal classification -AffectiveTweets (Section 3) and Brainy (Section 4). For the regression task we just use the AffectiveTweets system. We train a separate model for each emotion. The Brainy system performed better in our pre-evaluation experiments on the development data for all emotions in Spanish and for fear and joy emotions in Arabic.", | |
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| "section": "System Description", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "Tweets often contain slang expressions, misspelled words, emoticons or abbreviations and it's needed to make some preprocessing steps before extracting features. First, every tweet was tokenized using TweetNLP 1 (Gimpel et al., 2011) . Then the AffectiveTweets 2 package for Weka machine learning workbench (Hall et al., 2009) was used for feature extraction. The following steps were applied on tokens for every language in both tasks:", | |
| "cite_spans": [ | |
| { | |
| "start": 212, | |
| "end": 233, | |
| "text": "(Gimpel et al., 2011)", | |
| "ref_id": "BIBREF7" | |
| }, | |
| { | |
| "start": 307, | |
| "end": 326, | |
| "text": "(Hall et al., 2009)", | |
| "ref_id": "BIBREF9" | |
| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Tweets Preprocessing", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "1. Tokens were converted to lowercase 2. URL links were replaced with http://www.url.com token 3. Twitter usernames (tokens starting with @)", | |
| "cite_spans": [], | |
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| "eq_spans": [], | |
| "section": "Tweets Preprocessing", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "were replaced with @user token 4. Tokens containing sequences of letters occurring more than two times in a row were replaced with two occurrences of them (e.g. huuuungry is reduced to huungry, looooove to loove)", | |
| "cite_spans": [], | |
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| "section": "Tweets Preprocessing", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "5. Common sequences of words and emojis were divided by space (e.g. token \" nice:D:D\" was divided into two tokens \" nice\" and \" :D:D\")", | |
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| "section": "Tweets Preprocessing", | |
| "sec_num": "3.1" | |
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| { | |
| "text": "These steps lead to reduction of feature space as shown in (Go et al., 2009) . We also used some individual preprocessing for Arabic language. After the above described steps every token was also processed via Stanford Word Segmenter 3 (Monroe et al., 2014) . When using word embeddings, we transformed Arabic words from regular UTF-8 Arabic to a more ambiguous form 4 . This was done only for word embedding features.", | |
| "cite_spans": [ | |
| { | |
| "start": 59, | |
| "end": 76, | |
| "text": "(Go et al., 2009)", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 236, | |
| "end": 257, | |
| "text": "(Monroe et al., 2014)", | |
| "ref_id": "BIBREF23" | |
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| "section": "Tweets Preprocessing", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "Our AffectiveTweets system used combinations of features that are described in this section. The submitted combination of features is shown in Table 1 .", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 143, | |
| "end": 151, | |
| "text": "Table 1", | |
| "ref_id": "TABREF1" | |
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| ], | |
| "eq_spans": [], | |
| "section": "Features", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "\u2022 Word n-grams (WN n i ): word n-grams 5 from i to n (for i = 1, n = 2, unigrams and bigrams were used).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Features", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "\u2022 Character n-grams (ChN n i ): character ngrams 5 from i to n (for i = 2, n = 3 character bigrams and trigrams were used).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Features", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "\u2022 Word Embeddings (WE): an average of the word embeddings of all the words in a tweet.", | |
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| "section": "Features", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "\u2022 Affective Lexicons (L): we used Affective-Tweets package to extract features from affective lexicons. In every language we also used SentiStrength (L-se) lexcion-based method (Thelwall et al., 2012) .", | |
| "cite_spans": [ | |
| { | |
| "start": 177, | |
| "end": 200, | |
| "text": "(Thelwall et al., 2012)", | |
| "ref_id": "BIBREF33" | |
| } | |
| ], | |
| "ref_spans": [], | |
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| "section": "Features", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "\u2022 LDA -Latent Dirichlet Allocation (D n ): topic distribution of tweet, that is obtained from our pre-trained model, n indicates number of topics in model (for n = 5, feature vector with dimension 5 will be produced and each component of the vector refers to one topic). We used LDA features only in Affecti-veTweets system.", | |
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| "section": "Features", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "\u2022 Ultradense Word Embeddings (WE-ue): Rothe et al. (2016) created embeddings in the Twitter domain.", | |
| "cite_spans": [ | |
| { | |
| "start": 38, | |
| "end": 57, | |
| "text": "Rothe et al. (2016)", | |
| "ref_id": "BIBREF28" | |
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| "section": "English Word Embeddings:", | |
| "sec_num": "3.2.1" | |
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| { | |
| "text": "\u2022 Baseline Word Embeddings (WE-b): Mohammad and Bravo-Marquez (2017) created embeddings from the Edinburgh Twitter Corpus (Petrovi\u0107 et al., 2010 Mentioned Arabic word embeddings were created with Global Vectors (GloVe) (Pennington et al., 2014) and Word2Vec toolkit (Mikolov et al., 2013) using skip-gram (SG) model and continuous bagof-words (CBOW) model. These Arabic word embeddings were trained on different data domains -Twitter (tw), web pages (web), Wikipedia (wiki), and their combination (var) for more details see the cited papers.", | |
| "cite_spans": [ | |
| { | |
| "start": 122, | |
| "end": 144, | |
| "text": "(Petrovi\u0107 et al., 2010", | |
| "ref_id": "BIBREF27" | |
| }, | |
| { | |
| "start": 219, | |
| "end": 244, | |
| "text": "(Pennington et al., 2014)", | |
| "ref_id": "BIBREF26" | |
| }, | |
| { | |
| "start": 266, | |
| "end": 288, | |
| "text": "(Mikolov et al., 2013)", | |
| "ref_id": "BIBREF14" | |
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| "section": "English Word Embeddings:", | |
| "sec_num": "3.2.1" | |
| }, | |
| { | |
| "text": "-We used all affective lexicons from the Af-fectiveTweets package.", | |
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| "section": "English lexicons (L-en):", | |
| "sec_num": "3.2.4" | |
| }, | |
| { | |
| "text": "-Translated NRC Word-Emotion Association Lexicon (Mohammad and Turney, 2013) -Emotion Lexicon (Sidorov et al., 2012) - -LYSA Twitter lexicon (Vilares et al., 2014) 3.2.6 Arabic lexicons (L-ar): (Mohammad and Turney, 2013; Mohammad et al., 2016a; Salameh et al., 2015; Mohammad et al., 2016b) .", | |
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| { | |
| "start": 49, | |
| "end": 76, | |
| "text": "(Mohammad and Turney, 2013)", | |
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| "start": 94, | |
| "end": 116, | |
| "text": "(Sidorov et al., 2012)", | |
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| "start": 141, | |
| "end": 163, | |
| "text": "(Vilares et al., 2014)", | |
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| "start": 194, | |
| "end": 221, | |
| "text": "(Mohammad and Turney, 2013;", | |
| "ref_id": "BIBREF21" | |
| }, | |
| { | |
| "start": 222, | |
| "end": 245, | |
| "text": "Mohammad et al., 2016a;", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 246, | |
| "end": 267, | |
| "text": "Salameh et al., 2015;", | |
| "ref_id": "BIBREF29" | |
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| "start": 268, | |
| "end": 291, | |
| "text": "Mohammad et al., 2016b)", | |
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| "section": "Spanish lexicons (L-es):", | |
| "sec_num": "3.2.5" | |
| }, | |
| { | |
| "text": "-Translated NRC Word-Emotion Association Lexicon -Translation of Bing Liu's Lexicon -Arabic Emoticon Lexicon -Arabic Hashtag Lexicon Regression English Arabic Spanish anger L-en, D 500 var-SG, L-ar, D 250 , WN 1 1 L-en, L-es, WE-us, WN 1 1 fear L-en, L-se, WE-b var-SG, L-ar, D 250 L-en, L-es, L-se, WE-us, WN 1 1 , D 1000 joy L-en, L-se, WE-b var-SG, L-ar D 250 L-es, WE-us, WN 2 1 , ChN 3 2 sadness L-en, L-se, WE-b var-SG, L-ar L-en, L-es, L-se, WE-us, WN 2 1 , D 1000 Classification anger L-en, D 250 WN 1 1 fear L-en, L-se, WE-b, WN 2 1 , D 250 joy L-en, L-se, WE-b, WN 2 1 sadness L-en, L-se, D 250 var-CBOW, L-ar, L-se, WN 1 1 , D 250", | |
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| "section": "Spanish lexicons (L-es):", | |
| "sec_num": "3.2.5" | |
| }, | |
| { | |
| "text": "In our AffectiveTweets system we used an L 2regularized L 2 -loss SVM regression and classification model with the regularization parameter C set to 1, implemented in LIBLINEAR Library (Fan et al., 2008) 6 .", | |
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| "section": "Model Training", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "To use topics created with LDA (Latent Dirichlet Allocation) (Blei et al., 2003) as features, we trained our own models for every language. Tweets used to train the Arabic and Spanish models were taken from SemEval-2018 AIT DISC corpus and tweets for English model were taken from Sentiment140 7 training data (Go et al., 2009) . We trained our LDA models with LDA implementation from MALLET 8 (McCallum, 2002) . We used the same preprocessing for LDA as for regular feature extraction. Additionally we removed stopwords and following special characters [ , . ! -] . Tokens from Spanish tweets were stemmed with Snowball 9 stemming algorithm.", | |
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| "end": 80, | |
| "text": "(Blei et al., 2003)", | |
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| "text": "(Go et al., 2009)", | |
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| "text": "(McCallum, 2002)", | |
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| "text": "[ , . ! -]", | |
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| "section": "LDA Training", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "We use Maximum Entropy classifier from Brainy machine learning library (Konkol, 2014) and UD-6 https://www.csie.ntu.edu.tw/\u02dccjlin/ liblinear/ 7 http://help.sentiment140.com/ 8 http://mallet.cs.umass.edu/ 9 http://snowballstem.org/", | |
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| "start": 71, | |
| "end": 85, | |
| "text": "(Konkol, 2014)", | |
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| "section": "Brainy System", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "Pipe (Straka et al., 2016) for preprocessing and doesn't use any lexicons, just word embeddings. The system is based on (Hercig et al., 2016) .", | |
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| { | |
| "start": 5, | |
| "end": 26, | |
| "text": "(Straka et al., 2016)", | |
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| "text": "(Hercig et al., 2016)", | |
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| "section": "Brainy System", | |
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| "text": "The same preprocessing has been done for all datasets. We use UDPipe (Straka et al., 2016) with Spanish Universal Dependencies 1.2 models and Arabic Universal Dependencies 2.0 models for POS tagging and lemmatization. Tokenization has been done by TweetNLP tokenizer (Owoputi et al., 2013) . We further replace all user mentions with the token \"@USER\" and all links with the token \"$LINK\".", | |
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| { | |
| "start": 69, | |
| "end": 90, | |
| "text": "(Straka et al., 2016)", | |
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| "section": "Preprocessing", | |
| "sec_num": "4.1" | |
| }, | |
| { | |
| "text": "The Brainy system used the following features. The exact combination of features for each emotion and the change in performance caused by its removal is shown in Table 9 .", | |
| "cite_spans": [], | |
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| "start": 162, | |
| "end": 169, | |
| "text": "Table 9", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
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| { | |
| "text": "\u2022 Character n-grams (ChN n ): Separate binary feature for each character n-gram in the utterance text. We do it separately for different orders n \u2208 {1, 2, 3, 4, 5} and remove ngrams with frequency t.", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 Bag of Words (BoW): We used bag-ofwords representation of a tweet, i.e. separate binary feature representing the occurrence of a word in the tweet.", | |
| "cite_spans": [], | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 Bag of Morphological features (BoM): for all verbs in the tweet. The morphological features 10 include abbreviation, aspect, definiteness, degree of comparison, evidentiality, mood, polarity, politeness, possessive, pronominal type, tense, verb form, and voice.", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 Bag of POS (BoPOS): We used bag-ofwords representation of a tweet, i.e. separate binary feature representing the occurrence of a POS tag in the tweet.", | |
| "cite_spans": [], | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 Bag of Parse Tree Tags (BoT): We used bag-of-words representation of a tweet, i.e. separate binary feature representing the occurrence of a parse tree tag in the tweet. We remove tags with a frequency \u2264 2.", | |
| "cite_spans": [], | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 Emoticons (E): We used a list of positive and negative emoticons (Montejo-R\u00e1ez et al., 2012) . The feature captures the presence of an emoticon within the text.", | |
| "cite_spans": [ | |
| { | |
| "start": 67, | |
| "end": 94, | |
| "text": "(Montejo-R\u00e1ez et al., 2012)", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 First Words (FW): Bag of first five words with at least 2 occurrences.", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
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| { | |
| "text": "\u2022 Last Words (LW): Bag of last five words with at least 2 occurrences.", | |
| "cite_spans": [], | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
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| { | |
| "text": "\u2022 Last BoM (LBoM): Bag of last five morphological features (see BoM) with at least 2 occurrences.", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 FastText (FT): An average of the FastText (Bojanowski et al., 2016) word embeddings of all the words in a tweet.", | |
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| "start": 44, | |
| "end": 69, | |
| "text": "(Bojanowski et al., 2016)", | |
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| }, | |
| { | |
| "text": "\u2022 N-gram Shape (NSh): The occurrence of word shape n-gram in the tweet. Word shape assigns words into one of 24 classes 11 similar to the function specified in (Bikel et al., 1997) . We consider unigrams, bigrams, and trigrams with frequency \u2264 2.", | |
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| "start": 160, | |
| "end": 180, | |
| "text": "(Bikel et al., 1997)", | |
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| "section": "Features", | |
| "sec_num": "4.2" | |
| }, | |
| { | |
| "text": "\u2022 POS Count Bins (POS-B): We map the frequency of POS tags in a tweet into a onehot vector with length three and use this vector as binary features for the classifier.", | |
| "cite_spans": [], | |
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| }, | |
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| "text": "The frequency belongs to one of three equalfrequency bins 12 . Each bin corresponds to a position in the vector. We remove POS tags with frequency t \u2264 5.", | |
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| "text": "\u2022 TF-IDF: Term frequency -inverse document frequency of a word computed from the training data for words with at least 5 occurrences and at most 50 occurrences. \u2022 Text Length Bins (TL-B): We map the tweet length into a one-hot vector with length three and use this vector as binary features for the classifier. The length of a tweet belongs to one of three equal-frequency bins 12 . Each bin corresponds to a position in the vector.", | |
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| "section": "Features", | |
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| }, | |
| { | |
| "text": "\u2022 Verb Bag of Words (V-BoW): Bag of words for parent, siblings, and children of the verb from the sentence parse tree. ", | |
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| "section": "Features", | |
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| }, | |
| { | |
| "text": "All presented experiments are evaluated on the test data for the given task.", | |
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| "section": "Experiments", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We performed ablation experiments to see which features are the most beneficial (see Table 9 , 8, and 10). Numbers represent the performance change when the given feature is removed 13 .", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 85, | |
| "end": 93, | |
| "text": "Table 9", | |
| "ref_id": "TABREF11" | |
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| ], | |
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| "section": "Experiments", | |
| "sec_num": "5" | |
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| { | |
| "text": "Word embeddings features have a great impact on system performance, so we compared several word embeddings for every language (Table 2, 3, and 4). For English was best WE-ue word embeddings, but for submission we used WE-b word embeddings, because it worked better on dev data. In Spanish tweets the WE-us word embeddings outperformed the WE-ft word embeddings in regression and WE-us was better for classification in anger and on average of all emotions. For classification in Arabic was var-CBOW best on every emotion except anger and for regression var-SG worked best on average and on fear.", | |
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| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We also experimented with only LDA features to find out how the numbers of topics in LDA model affect the performance (see Figure 1) . We star- 13 The lowest number denotes the most beneficial feature ted with models containing 5 topics and continued up to 1000 (step was non-equidistantly increased). Our experiments suggest that the best setting is around 200-300 topics. We selected the number of topics based on the performance on the development data.", | |
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| "start": 144, | |
| "end": 146, | |
| "text": "13", | |
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| "ref_spans": [ | |
| { | |
| "start": 123, | |
| "end": 132, | |
| "text": "Figure 1)", | |
| "ref_id": "FIGREF2" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Experiments", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Our results in the emotion intensity regression subtask are in Table 5 and our results in the emotion intensity ordinal classification subtask are in Table 6 and Table 7 . The system settings and features for each language and emotion were selected based on our pre-evaluation experiments with evaluation on the development data.", | |
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| { | |
| "start": 63, | |
| "end": 70, | |
| "text": "Table 5", | |
| "ref_id": "TABREF7" | |
| }, | |
| { | |
| "start": 150, | |
| "end": 169, | |
| "text": "Table 6 and Table 7", | |
| "ref_id": "TABREF8" | |
| } | |
| ], | |
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| "section": "Results", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "We competed in the emotion intensity regression and ordinal classification tasks in English, Arabic and Spanish.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Conclusion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "Our ranks are 27 th out of 48 for English, 5 th out of 14 for Arabic, and 5 th out of 16 for Spanish for the regression task and 21 st out of 39 for English, 5 th out of 14 for Arabic, and 5 th out of 16 for Spanish for the ordinal classification task. ", | |
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| "section": "Conclusion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "http://www.cs.cmu.edu/\u02dcark/TweetNLP/ 2 https://affectivetweets.cms.waikato. ac.nz/", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "https://nlp.stanford.edu/software/ segmenter.shtml 4 Some characters were replaced, for more details see(Soliman et al., 2017).5 Value of each feature is set to its frequency in the tweet", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "http://universaldependencies.org/u/ feat/index.html", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "We use edu.stanford.nlp.process.WordShapeClassifier with the WORDSHAPECHRIS1 setting available in Standford CoreNLP library.12 The frequencies from the training data are split into three equal-size bins according to 33% quantiles.", | |
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| ], | |
| "back_matter": [ | |
| { | |
| "text": "This publication was supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports under the program NPU I and", | |
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| "eq_spans": [], | |
| "section": "Acknowledgments", | |
| "sec_num": null | |
| } | |
| ], | |
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| "FIGREF0": { | |
| "num": null, | |
| "text": "Polarity lexicon (Urizar and Roncal, 2013) -Expanded Word-Emotion Association Lexicon (Bravo-Marquez et al., 2016) (we translated this lexicon to Spanish) -iSOL (Molina-Gonz\u00e1lez et al., 2013) -ML-SentiCon (Cruz et al., 2014)-Ultradense lexicon(Rothe et al., 2016)", | |
| "uris": null, | |
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| "num": null, | |
| "text": "LDA performance based on number of topics, the y-axis denotes Pearson correlation", | |
| "uris": null, | |
| "type_str": "figure" | |
| }, | |
| "TABREF1": { | |
| "text": "Used features in the AffectiveTweets system", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>-Arabic Hashtag Lexicon (dialectal)</td></tr><tr><td>-Translated NRC Hashtag Sentiment Lexicon</td></tr><tr><td>-SemEval-2016 Arabic Twitter Lexicon</td></tr><tr><td>Lexicons are described in</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF3": { | |
| "text": "Arabic embeddings experiments results", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"5\">Emotion intensity regression -Pearson (all instances)</td></tr><tr><td>embeddings</td><td>avg</td><td>anger fear</td><td>joy</td><td>sadness</td></tr><tr><td>WE-us WE-ft</td><td colspan=\"4\">0.559 0.464 0.581 0.581 0.611 0.510 0.369 0.577 0.528 0.565</td></tr><tr><td colspan=\"5\">Emotion intensity classification -Pearson (all classes)</td></tr><tr><td>WE-us WE-ft</td><td colspan=\"4\">0.429 0.422 0.382 0.478 0.434 0.407 0.256 0.428 0.481 0.462</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF4": { | |
| "text": "Spanish embeddings experiments results", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"5\">Emotion intensity regression -Pearson (all instances)</td></tr><tr><td>embeddings</td><td>avg</td><td>anger fear</td><td>joy</td><td>sadness</td></tr><tr><td>WE-ue WE-b</td><td colspan=\"4\">0.598 0.594 0.595 0.586 0.593 0.541 0.475 0.549 0.456 0.505</td></tr><tr><td colspan=\"5\">Emotion intensity classification -Pearson (all classes)</td></tr><tr><td>WE-ue WE-b</td><td colspan=\"4\">0.479 0.412 0.507 0.438 0.459 0.456 0.212 0.499 0.336 0.376</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF5": { | |
| "text": "English embeddings experiments results", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table/>", | |
| "num": null | |
| }, | |
| "TABREF7": { | |
| "text": "Pearson correlation for the emotion intensity regression task", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>Subtask</td><td>System</td><td>macro-avg</td><td colspan=\"3\">Pearson (all classes) anger fear</td><td>joy</td><td>sadness</td><td>macro-avg</td><td colspan=\"3\">Pearson (some-emotion) anger fear joy</td><td>sadness</td></tr><tr><td colspan=\"13\">EI-oc-EN AffectiveTweets 0.506 (21) 0.477 (23) 0.470 (17) 0.555 (19) 0.522 (22) 0.346 (23) 0.308 (25) 0.273 (21) 0.452 (21) 0.350 (25)</td></tr><tr><td>EI-oc-AR</td><td>AT&Brainy</td><td>0.394 (5)</td><td>0.327 (5)</td><td>0.345 (5)</td><td colspan=\"2\">0.437 (5)</td><td>0.467 (5)</td><td>0.280 (5)</td><td>0.246 (6)</td><td>0.246 (6)</td><td>0.351 (5)</td><td>0.277 (7)</td></tr><tr><td>EI-oc-ES</td><td>Brainy</td><td>0.504 (5)</td><td>0.361 (7)</td><td>0.606 (3)</td><td colspan=\"2\">0.544 (5)</td><td>0.506 (5)</td><td>0.410 (5)</td><td>0.267 (6)</td><td>0.499 (2)</td><td>0.420 (6)</td><td>0.452 (5)</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF8": { | |
| "text": "Pearson correlation for the emotion intensity ordinal classification task", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td>Subtask</td><td>System</td><td>macro-avg</td><td colspan=\"3\">Kappa (all classes) anger fear</td><td>joy</td><td>sadness</td><td>macro-avg</td><td colspan=\"3\">Kappa (some-emotion) anger fear</td><td>joy</td><td>sadness</td></tr><tr><td colspan=\"13\">EI-oc-EN AffectiveTweets 0.494 (21) 0.467 (19) 0.450 (14) 0.548 (17) 0.510 (19) 0.290 (23) 0.269 (23) 0.166 (20) 0.420 (20) 0.303 (24)</td></tr><tr><td>EI-oc-AR</td><td>AT&Brainy</td><td>0.386 (5)</td><td>0.324 (5)</td><td>0.327 (5)</td><td colspan=\"2\">0.428 (5)</td><td>0.464 (5)</td><td>0.241 (5)</td><td>0.219 (5)</td><td>0.178 (5)</td><td colspan=\"2\">0.340 (5)</td><td>0.226 (5)</td></tr><tr><td>EI-oc-ES</td><td>Brainy</td><td>0.475 (5)</td><td>0.432 (5)</td><td>0.544 (6)</td><td colspan=\"2\">0.447 (8)</td><td>0.477 (6)</td><td>0.340 (6)</td><td>0.299 (5)</td><td>0.405 (5)</td><td colspan=\"2\">0.302 (8)</td><td>0.353 (6)</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF9": { | |
| "text": "Cohen's kappa for the emotion intensity ordinal classification task Results achieved with all used features for given emotion \u2020 ALL without used LDA feature.", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"5\">Emotion intensity classification -Pearson (all classes)</td></tr><tr><td>Feature</td><td colspan=\"3\">Arabic anger sadness anger</td><td>English fear joy</td><td>sadness</td></tr><tr><td>ALL *</td><td>0.327 \u2021</td><td>0.467</td><td colspan=\"2\">0.477 0.470 0.555 \u2021</td><td>0.522</td></tr><tr><td>-D \u2020 250</td><td/><td colspan=\"3\">0.467 \u2021 0.490 \u2021 0.467 \u2021</td><td>0.497 \u2021</td></tr><tr><td>L-en</td><td/><td/><td colspan=\"3\">0.000 -0.090 -0.007 -0.140</td></tr><tr><td>L-se</td><td/><td>-0.019</td><td/><td>-0.023 0.008</td><td>-0.030</td></tr><tr><td>WN 2 1 WE-b</td><td/><td/><td/><td>-0.055 -0.028 0.001 0.006</td></tr><tr><td>WN 1 1 L-ar</td><td>0.000</td><td>0.098 -0.038</td><td/><td/></tr><tr><td>var-CBOW</td><td/><td>-0.106</td><td/><td/></tr><tr><td/><td/><td/><td/><td/></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF10": { | |
| "text": "AffectiveTweets feature ablation study", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td/><td colspan=\"6\">Emotion intensity classification -Pearson (all classes)</td></tr><tr><td>Feature</td><td colspan=\"2\">Arabic fear</td><td>joy</td><td>anger</td><td>Spanish fear joy</td><td>sadness</td></tr><tr><td>BoW</td><td colspan=\"3\">-0.013 0.022</td><td colspan=\"3\">0.005 -0.041 0.018</td><td>0.003</td></tr><tr><td colspan=\"7\">-0.017 0.024 0.034 -0.037 -0.009 0.010 -0.053 0.011 0.016 -0.041 0.011 0.009 0.018 ChN 4,5 t \u2264 2 -0.067 -0.036 -0.008 -0.056 -0.050 -0.011 ChN 1 t \u2264 5 0.014 ChN 2 t \u2264 5 0.005 ChN 3 t \u2264 5 BoM -0.022 -0.013 0.017 -0.011</td></tr><tr><td>E</td><td>0.011</td><td/><td/><td>-0.007</td><td/></tr><tr><td>FT</td><td colspan=\"4\">-0.027 -0.008 0.006</td><td colspan=\"2\">-0.004</td></tr><tr><td>BoPOS</td><td>-0.015</td><td/><td/><td colspan=\"2\">0.008 -0.010</td><td>-0.002</td></tr><tr><td>POS-B</td><td colspan=\"5\">-0.008 -0.025 -0.010 -0.013</td><td>0.013</td></tr><tr><td>BoT</td><td>0.017</td><td colspan=\"4\">0.006 -0.003 -0.010</td><td>0.018</td></tr><tr><td>TF-IDF</td><td>-0.017</td><td/><td/><td>-0.004</td><td colspan=\"2\">0.009</td></tr><tr><td>NSh</td><td>0.010</td><td colspan=\"3\">0.006 -0.011</td><td colspan=\"2\">0.002</td><td>-0.008</td></tr><tr><td>FW</td><td/><td/><td/><td>-0.001</td><td colspan=\"2\">0.002</td><td>0.010</td></tr><tr><td>LW</td><td/><td/><td/><td>-0.007</td><td colspan=\"2\">-0.014 -0.003</td></tr><tr><td>TL-B</td><td/><td/><td/><td/><td/><td>-0.004</td></tr><tr><td>LBoM</td><td>0.036</td><td/><td/><td>0.000</td><td/><td>0.005</td></tr><tr><td>V-BoW</td><td>-0.006 *</td><td/><td/><td>-0.005 \u2020</td><td colspan=\"2\">0,003 \u2021</td></tr><tr><td>* adverb</td><td colspan=\"5\">\u2020 adverb, noun, adjective, verb, auxiliary</td><td>\u2021 noun</td></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF11": { | |
| "text": "Brainy feature ablation study Results achieved with all used features. \u2020 ALL without used LDA feature.", | |
| "type_str": "table", | |
| "html": null, | |
| "content": "<table><tr><td/><td colspan=\"3\">Emotion intensity regression -</td></tr><tr><td/><td colspan=\"3\">Pearson (all instances)</td></tr><tr><td>Feature</td><td>anger</td><td colspan=\"2\">English fear joy</td><td>sadness</td></tr><tr><td>ALL *</td><td colspan=\"4\">0.640 0.642 \u2021 0.652 \u2021 0.636 \u2021</td></tr><tr><td>-D \u2020 500</td><td>0.634 \u2021</td><td/><td/></tr><tr><td>L-en</td><td colspan=\"4\">0.000 -0.044 -0.031 -0.087</td></tr><tr><td>L-se</td><td/><td colspan=\"3\">-0.037 -0.010 -0.013</td></tr><tr><td>WE-b</td><td/><td colspan=\"3\">-0.020 -0.040 -0.017</td></tr><tr><td/><td/><td colspan=\"2\">Arabic</td></tr><tr><td>ALL *</td><td colspan=\"3\">0.487 0.559 0.619</td><td>0.631</td></tr><tr><td>-D \u2020 250</td><td colspan=\"3\">0.479 0.558 0.604</td></tr><tr><td>L-ar</td><td colspan=\"4\">0.020 0.011 -0.027 -0.027</td></tr><tr><td colspan=\"5\">WN 1 1 var-SG -0.010 -0.244 -0.197 -0.196 0.036</td></tr><tr><td/><td/><td colspan=\"2\">Spanish</td></tr><tr><td>ALL *</td><td colspan=\"3\">0.542 0.688 0.646</td><td>0.644</td></tr><tr><td>-D \u2020 1000</td><td/><td>0.688</td><td/><td>0.639</td></tr><tr><td>L-en</td><td colspan=\"2\">0.008 0.006</td><td/><td>-0.007</td></tr><tr><td>L-es</td><td colspan=\"4\">-0.016 0.005 -0.042 -0.009</td></tr><tr><td>L-se</td><td/><td>0.002</td><td/><td>-0.001</td></tr><tr><td colspan=\"5\">WE-us -0.021 -0.027 -0.017 -0.030</td></tr><tr><td>WN 1 1 WN 2 1 ChN 3 2</td><td colspan=\"2\">-0.033 -0.093</td><td colspan=\"2\">-0.050 -0.013 -0.006</td></tr><tr><td/><td/><td/><td/></tr></table>", | |
| "num": null | |
| }, | |
| "TABREF12": { | |
| "text": "AffectiveTweets feature ablation study. by university specific research project SGS-2016-018 Data and Software Engineering for Advanced Applications.", | |
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| "html": null, | |
| "content": "<table/>", | |
| "num": null | |
| } | |
| } | |
| } | |
| } |