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    Plan

    Plan détaillé Texte intégral 1 Introduction 2 Description of the Systm 3 Experiments 4 Results 5 Discussion Bibliographie Notes de bas de page Auteurs

    EVALITA. Evaluation of NLP and Speech Tools for Italian

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    Convolutional Neural Networks for Sentiment Analysis on Italian Tweets

    Giuseppe Attardi, Daniele Sartiano, Chiara Alzetta et Federica Semplici

    p. 156-160

    Résumés

    The paper describes our submission to the task 2 of SENTIment POLarity Classification in Italian Tweets at Evalita 2016. Our approach is based on a convolutional neural network that exploits both word embeddings and Sentiment Specific word embeddings. We also experimented a model trained with a distant supervised corpus. Our submission with Sentiment Specific word embeddings achieved the first official score.

    L’articolo descrive la nostra partecipazione al task 2 di SENTIment POLarity Classification in Italian Tweets a Evalita 2016. Il nostro approccio si basa su una rete neurale convoluzionale che sfrutta sia word embeddings tradizionali che sentiment specific word embeddings. Abbiamo inoltre sperimentato un modello allenato su un corpus costruito mediante tecnica distant supervised. Il nostro sistema, che utilizza Specific Sentiment word embeddings, ha ottenuto il primo punteggio officiale.

    Remerciements

    We gratefully acknowledge the support of the University of Pisa through project PRA and NVIDIA Corporation for a donation of the Tesla K40 GPU used in the experiments.
    Integris srl cooperated by providing a corpus of sentiment annotated tweets.

    Texte intégral 1 Introduction 2 Description of the Systm 2.1 Data Preprocessing 2.2 Word Embeddings and Sentiment Specific Word Embeddings 2.3 Distant supervision 3 Experiments 4 Results 5 Discussion Bibliographie Notes de bas de page Auteurs

    Texte intégral

    1 Introduction

    1The paper describes our submissions to the Task 2 of SENTiment POLarity Classification at Evalita 2016 (Barbieri et al. 2016).

    2In Sentipolc the focus is the sentiment analysis of the in Italian tweets, it is divided in three subtasks:

    • Task 1: Subjectivity Classification: identify the subjectivity of a tweet.

    • Task 2: Polarity Classification: classify a tweet as positive, negative, neutral or mixed (i.e. a tweet with positive and negative sentiment).

    • Task 3: Irony Detection: identify if is present the irony in a tweet.

    3The state of the art on the polarity classification of tweets is the application of Deep Learning methods (Nakov et al., 2016), like convolutional neural network or recurrent neural networks, in particular long short-term memory networks (Hochreiter, and Schmidhuber, 1997).

    4We explored Deep Learning techniques for the sentiment analysis of English tweets at Semeval 2016 with good results, where we noticed that use of convolutional neural network and Sentiment Specific word embeddings was promising.

    5We applied a similar approach for the Italian language, building word embeddings from a big corpus of Italian tweets, sentiment specific word embeddings from positive and negative tweets, using a convolutional neural network as classifier. We also introduced a distant supervised corpus as silver training set.

    6We report the results of our experiments with this approach on the task Evalita 2016 Sentipolc Task 2 Polarity classification.

    2 Description of the Systm

    7The architecture of the system consists of the following steps:

    • build word embeddings from a collection of 167 million tweets collected with the Twitter API over a period of May to September 2016, preprocessed as described later.

    • build Sentiment Specific word embeddings using a portion of these tweets split into positive/negative by distant supervision.

    • train a convolutional neural network classifier using one of the above word embeddings

    Figure 1. The Deep Learning classifier.

    Image 1000000000000C7E0000049D126933B1.jpg

    8The convolutional neural network classifier exploits pre-trained word embeddings as only features in various configurations as described below. The architecture of the classifier consists of the following layers described in Figure 1: a lookup layer for word embeddings, a convolutional layer with a ReLU activation function, a maxpooling layer, a dropout layer, a linear layer with tanh activation and a softmax layer. This is the same classifier described in (Attardi and Sartiano, 2016), that achieved good results at the SemEval 2016 task 4 on Sentiment Analysis in Twitter (Nakov et al., 2016). Here we test it on a similar task for Italian tweets.

    2.1 Data Preprocessing

    9In order to build the word embeddings we preprocessed the tweets using tools from the Tanl pipeline (Attardi et al., 2010): the sentence splitter and the specialized tweet tokenizer for the tokenization and the normalization of tweets. Normalization involved replacing the mentions with the string “@mention”, emoticons with their name (e.g. “EMO_SMILE”) and URLs with “URL_NORM”.

    2.2 Word Embeddings and Sentiment Specific Word Embeddings

    10We experimented with standard word embeddings, in particular building them with the tool word2vec1 (Mikolov, 2013), using the skip-gram model. These word embeddings though do not take into account semantic differences between words expressing opposite polarity, since they basically encode co-occurrence information as shown by (Levy and Goldberg, 2014). For encodes sentiment information in the continuous representation of words, we use the technique of Tang et al. (2014) as implemented in the DeepNL2 library (Attardi, 2015). A neural network with a suitable loss function provides the supervision for transferring the sentiment polarity of texts into the embeddings from generic tweets.

    2.3 Distant supervision

    11The frequency distribution of classes in the dataset, as shown in Table 1, seems skewed and not fully representative of the distribution in a statistical sample of tweets: negative tweets are normally much less frequent than positive or neutral ones (Bravo-Marquez, 2015). To reduce this bias and to increase the size of the training set, we selected additional tweets from our corpus of Italian tweets by means distant supervision. In the first step we selected the tweets belonging to a class (positive, negative, neutral, mixed) via regular expressions. In the second step, the selected tweets are classified by the classifier trained using the task trainset. The silver corpus is built taking the tweets with the matched class between the regular expression system and the classifier.

    3 Experiments

    12The plain word embeddings were built applying vord2vec to a collection of 167 million Italian unlabeled tweets, using the the skip gram model, and the following parameters: embeddings size 300, window dimension 5, discarding word that appear less than 5 times. We obtained about 450k word embeddings.

    13The sentiment specific word embeddings (SWE) were built with DeepNL, starting from the word embeddings built at the previous step and tuning them with a supervised set of positive or negative tweets, obtained as follows from 2.3 million tweets selected randomly from our corpus of collected tweets:

    • Positive tweet: one that contains only emoticons from a set of positive emoticons (e.g. smiles, hearts, laughs, expressions of surprise, angels and high fives).

    • Negative tweet: one that contains only emoticons from a set of negative emotions (e.g. tears, angry and sad).

    14Integris srl cooperated to the task providing a set of 1.3 million tweets, selected by relying on a lexicon of handcrafted polarized words. This resource is also added to the corpus.

    15We split the training set provided for the Evalita 2016 SentiPolc Task into a train set (5335 tweets), a validation set (592 tweets) and a test set (1482 tweets). This dataset was tokenized and normalized as described in Section 2.1.

    16For the take of participating to subtask 2, polarity classification, the 13-value annotations present in the datasets were converted into four values: “neutral”, “positive”, “negative” and “mixed” depending on the values of the fields “opos” and “oneg”, which express the tweet polarity, according to the task guidelines3. We did not take into account the values for “lpos” and “lneg”.

    17The frequency distribution of these classes turns out to be quite unbalanced, as shown in Table 1.

    Table 1. Task dataset distribution

    Class

    Train set

    Validation set

    Neutral

    2262

    554

    Negative

    2029

    513

    Positive

    1299

    312

    Mixed

    337

    103

    18The training set is still fairly small, compared for example to the size of the corpus used in SemEval 2106. The “mixed” class in particular is small in absolute numbers, even though not in percentage value, which makes hard to properly train a ML classifier.

    19Therefore we tried to increase the training set by means of the distant supervision as described above: we selected a maximum of 10,000 tweets for class via regular expressions, then we classified them with the classifier trained with the gold training set. We chose for addition into a silver training set, the tweets which were assigned by the classifier the same class of the regular expression. As reported in Table 2, the silver dataset remains unbalanced; in particular, no “mixed” example was added to the original train set.

    Table 2. Distant supervised dataset distribution.

    Class

    Train set

    Dev set

    Neutral

    8505

    554

    Negative

    5987

    513

    Positive

    6813

    312

    Mixed

    337

    103

    20Table 3 shows the common settings used for training the classifier. We used the same parameters as SemEval-2016.

    Table 3. Network Common Settings

    Word Embeddings Size

    300

    Hidden Units

    100

    Dropout Rate

    0.5

    Batch size

    50

    Adadelta Decay

    0.95

    Epochs

    50

    21We performed extensive experiments with the classifier in various configurations, varying the number of filters; the use of skip-gram word embeddings or sentiment specific word embeddings; different training sets, either the gold one or the silver one. Results of the evaluation on the validation set allowed us to choose the best settings, as listed in the Table 4. Best Settings.

    Table 4. Best Settings

    Run1

    Run2

    Embeddings

    WE skipgram

    SWE

    Training set

    Gold

    Silver

    Gold

    Silver

    Filters

    2,3,5

    4,5,6,7

    7,7,7,7,8,8,8,8

    7,8,9,10

    4 Results

    22We submitted four runs for the subtask 2 “polarity classification”:

    • UniPI_1.c: gold training set, word embeddings with skip-gram model, filters: “2,3,5”.

    • UniPI_1.u: silver corpus as training set, word embeddings with skip-gram model, filters: “4,5,6,7”.

    • UniPI_2.c: gold training set, sentiment specific word embeddings, filters: “7,7,7,7,8,8,8,8”.

    • UniPI_2.u: silver corpus as training set, sentiment specific word embeddings, filters: “7,8,9,10”.

    23The following table reports the top official results for the subtask 2:

    Table 5. Top official results for SentiPolc subtask 2.

    System

    Positive
    F-score

    Negative
    F-score

    Combined

    F-score

    UniPI_2.c

    0.685

    0.6426

    0.6638

    team1_1.u

    0.6354

    0.6885

    0.662

    team1_2.u

    0.6312

    0.6838

    0.6575

    team4_.c

    0.644

    0.6605

    0.6522

    team3_.1.c

    0.6265

    0.6743

    0.6504

    team5_2.c

    0.6426

    0.648

    0.6453

    team3_.2.c

    0.6395

    0.6469

    0.6432

    UniPI_1.u

    0.6699

    0.6146

    0.6422

    UniPI_1.c

    0.6766

    0.6002

    0.6384

    UniPI_2.u

    0.6586

    0.5654

    0.612

    24The run UniPI_2.c achieved the top overall score among a total of 26 submissions to task 2. This confirms the effectiveness of sentiment specific word embeddings in sentiment polarity classification also for Italian tweets.

    25The use of an extended silver corpus did not provide significant benefits, possibly because the resulting corpus was still unbalanced.

    26In addition to the subtask 2, we submitted one run for the Task 1 “Subjectivity Classification”: given a message, decide whether the message is subjective or objective. We used the same classifier for the subtask 2, using only two classes (subjective, objective), with the same skip-gram word embeddings used for the other task and the configuration listed in Table 3, using the following filters: “7,8,9,10”, without performing extensive experiments. The following table reports the top official results for the subtask 1:

    Table 6 Top official results for SentiPolc subtask 1.

    system

    Objective
    F-score

    Subjective
    F-score

    Combined
    F-score

    team1_1.u

    0.6784

    0.8105

    0.7444

    team1_2.u

    0.6723

    0.7979

    0.7351

    team2_.1.c

    0.6555

    0.7814

    0.7184

    team3_.2.c

    0.6733

    0.7535

    0.7134

    team4_.c

    0.6465

    0.775

    0.7107

    team5_2.c

    0.6671

    0.7539

    0.7105

    team6_.c

    0.6623

    0.755

    0.7086

    team1_.c

    0.6499

    0.759

    0.7044

    UniPI_1

    0.6741

    0.7133

    0.6937

    team3_.1.c

    0.6178

    0.735

    0.6764

    team8_.c

    0.5646

    0.7343

    0.6495

    team5_1.c

    0.6345

    0.6139

    0.6242

    5 Discussion

    27We confirmed the validity of the convolutional neural networks in the twitter sentiment classification, also for the Italian language.

    28The system achieved top score in the task 2 of SENTiment POLarity Classification Task of Evalita 2016.

    Bibliographie

    Giuseppe Attardi, Stefano Dei Rossi, and Maria Simi. 2010. The Tanl Pipeline. In Proc. of LREC Workshop on WSPP, Malta.

    Giuseppe Attardi. 2015. DeepNL: a Deep Learning NLP pipeline. Workshop on Vector Space Modeling for NLP. Proc. of NAACL HLT 2015, Denver, Colorado (June 5, 2015).

    Giuseppe Attardi and Daniele Sartiano. 2016. UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification. Proceedings of SemEval, 220-224.

    Francesco Barbieri, Valerio Basile, Danilo Croce, Malvina Nissim, Nicole Novielli, and Viviana Patti. 2016. Overview of the EVALITA 2016 SENTiment POLarity Classification Task. In Proceedings of the 5th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian (EVALITA 2016).

    Felipe Bravo-Marquez, Eibe Frank, and Bernhard Pfahringer. 2015. Positive, negative, or neutral: Learning an expanded opinion lexicon from emoticon-annotated tweets. IJCAI 2015. AAAI Press.

    Sepp Hochreiter, and Jürgen Schmidhuber. (1997). Long short-term memory. Neural computation 9.8 1735-1780.

    Omer Levy and Yoav Goldberg. 2014. Neural Word Embedding as Implicit Matrix Factorization. In Advances in neural information processing systems.

    Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems. arXiv:1310.4546

    Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, and Veselin Stoyanov. 2016. SemEval2016 task 4: Sentiment analysis in Twitter. In Proceedings of the 10th international workshop on semantic evaluation (SemEval 2016), San Diego, US (forthcoming).

    Duyu Tang, Furu Wei, Nan Yang, Ming Zhou, Ting Liu, and Bing Qin. 2014, June. Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. In ACL (1) (pp. 1555-1565).

    Notes de bas de page

    1 https://code.google.com/archive/p/word2vec/

    2 https://github.com/attardi/deepnl

    3 http://www.di.unito.it/~tutreeb/sentipolc-evalita16/sentipolc-guidelines2016UPDATED130916.pdf

    Auteurs

    • Giuseppe Attardi

      Dipartimento di Informatica Università di Pisa, Largo B. Pontecorvo, 3 I-56127 Pisa, Italy - attardi@di.unipi.it

    • Daniele Sartiano

      Dipartimento di Informatica Università di Pisa, Largo B. Pontecorvo, 3 I-56127 Pisa, Italy - sartiano@di.unipi.it

    • Chiara Alzetta

      Dipartimento di Informatica Università di Pisa, Largo B. Pontecorvo, 3 I-56127 Pisa, Italy - c.alzetta@studenti.unipi.it

    • Federica Semplici

      Dipartimento di Informatica Università di Pisa, Largo B. Pontecorvo, 3 I-56127 Pisa, Italy - f.semplici@studenti.unipi.it

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    1 https://code.google.com/archive/p/word2vec/

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    Format

    Attardi, G., Sartiano, D., Alzetta, C., & Semplici, F. (2016). Convolutional Neural Networks for Sentiment Analysis on Italian Tweets. In P. Basile, F. Cutugno, M. Nissim, V. Patti, & R. Sprugnoli (éds.), EVALITA. Evaluation of NLP and Speech Tools for Italian. Torino: Accademia University Press. https://doi.org/10.4000/books.aaccademia.1995
    Attardi, Giuseppe, Daniele Sartiano, Chiara Alzetta, et Federica Semplici. « Convolutional Neural Networks for Sentiment Analysis on Italian Tweets ». In EVALITA. Evaluation of NLP and Speech Tools for Italian, édité par Pierpaolo Basile, Franco Cutugno, Malvina Nissim, Viviana Patti, et Rachele Sprugnoli. Torino: Accademia University Press, 2016. doi:10.4000/books.aaccademia.1995.
    Attardi, Giuseppe, et al. « Convolutional Neural Networks for Sentiment Analysis on Italian Tweets ». EVALITA. Evaluation of NLP and Speech Tools for Italian, édité par Pierpaolo Basile et al., Accademia University Press, 2016, https://doi.org/10.4000/books.aaccademia.1995.

    Référence numérique du livre

    Format

    Basile, P., Cutugno, F., Nissim, M., Patti, V., & Sprugnoli, R. (éds.). (2016). EVALITA. Evaluation of NLP and Speech Tools for Italian. Torino: Accademia University Press. https://doi.org/10.4000/books.aaccademia.1899
    Basile, Pierpaolo, Franco Cutugno, Malvina Nissim, Viviana Patti, et Rachele Sprugnoli, éd. EVALITA. Evaluation of NLP and Speech Tools for Italian. Torino: Accademia University Press, 2016. doi:10.4000/books.aaccademia.1899.
    Basile, Pierpaolo, et al., éditeurs. EVALITA. Evaluation of NLP and Speech Tools for Italian. Accademia University Press, 2016, https://doi.org/10.4000/books.aaccademia.1899.
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