Pierpaolo Basile
email: pierpaolo.basile@uniba.it

Valerio Basile
email: valerio.basile@inria.fr

Elena Cabrio

Serena Villata

Argument Mining on Italian News Blogs

The goal of argument mining is to extract structured information, namely the arguments and their relations, from unstructured text. In this paper, we propose an approach to argument relation prediction based on supervised learning of linguistic and semantic features of the text. We test our method on the CorEA corpus of user comments to online newspaper articles, evaluating our system's performances in assigning the correct relation, i.e., support or attack, to pairs of arguments. We obtain results consistently better than a sentiment analysis-based baseline (over two out three correctly classified pairs), and we observe that sentiment and lexical semantics are the most informative features with respect to the relation prediction task.

Introduction

The argument mining [START_REF] Peldszus | From argument diagrams to argumentation mining in texts: A survey[END_REF][START_REF] Lippi | Argumentation mining: State of the art and emerging trends[END_REF] research area has recently become very relevant in computational linguistics. Its main goal is the automated extraction of natural language arguments and their relations from generic textual corpora, with the final goal of providing machine-readable structured data for computational models of argument and reasoning engines. Two main stages have to be considered in the typical argument mining pipeline, from the unstructured natural language documents towards structured (possibly machinereadable) data: (i) argument extraction, i.e., to detect arguments within the input natural language texts, and (ii) relation extraction, i.e., to predict what are the relations holding between the arguments identified in the first stage. The relation prediction task is extremely complex, as it involves high-level knowledge representation and reasoning issues. The relations between the arguments may be of heterogeneous nature, like attack, support or entailment [START_REF] Cabrio | A natural language bipolar argumentation approach to support users in online debate interactions †[END_REF].

The increasing amount of data available on the Web from heterogeneous sources, e.g., social network posts, forums, news blogs, and the specific form of language adopted there challenge argument mining methods, with the aim to support users to understand and interact with such a huge amount of information.

In this paper, we address this issue by presenting an argument relation prediction approach for Italian. We test the method on the CorEA corpus [START_REF] Celli | Corea: Italian news corpus with emotions and agreement[END_REF] of user comments to the news articles of an Italian newspaper, annotated with agreement (i.e., support) and disagreement (i.e., attack) relations. We extract argument-level features from the CorEA comment (i.e., argument) pairs, and we train our system to predict the support and attack relations.

Mining Arguments

A debate, whether it happens online or in person, can be modeled as a set of arguments proposed by the participants. Arguments can be independent, for instance expressing the participant's stance on a particular topic, but often they are replies to previous arguments put forward in the debate. This results in a network structure of the debate, that is, a (possibly disconnected) directed graph where nodes are arguments, and the two kinds of edges are the support and attack relations between them. In Figure 1, each node represents an argument with a numeric identifier, filled and dashed edges represent respectively support and attack relations, and dotted edges are neutral relations. The hublike node labeled 11 is a news article, thus attracting many first-level comments.

The goal of our work is to be able to predict the relations between the arguments in a given debate, thus reconstructing the relation graph. We therefore cast the problem as a classification task: given two arguments from a debate, we aim to predict whether one argument attacks the other, supports it, or there is no relation between the two arguments. The construction of the graph structure is then straightforward, resulting from the combination of all the argument pairs we considered.

Features

We extract argument-level features from the CorEA comment pairs, that we group into the following categories:

Lexical We take into account several lexical features: tokens, bi-grams, and the first bi-gram and tri-gram of each argument.

Syntactic We exploit the output of a dependency parser. We consider two kinds of dependency features: the former is the original output, the latter generalizes a word to its POS tag. For instance, "amod(denaro, pubblico)" is generalized as the "amod(NN, pubblico)" and "amod(denaro, ADJ)". We adopt the Malt parser [START_REF] Nivre | An efficient algorithm for projective dependency parsing[END_REF] trained on the Universal Dependency Treebank1 .

Message info

We extract the argument size, the number of uppercase words, the number of negations2 , the number of sequences of two or more punctuation characters, the number of citations. A citation is a quoted sequence of words in the second argument that occurs in the first argument.

Message overlap Cosine similarity between two arguments is computed exploiting TF/IDF.

Word-embedding

We build word-embeddings relying on the Paisà corpus through the word2vec [START_REF] Mikolov | Efficient estimation of word representations in vector space[END_REF] tool. We use a vector dimension equal to 50, and we consider only words that occur at least 20 times.

For each argument, we use the vector components as features directly.

Sentiment We extract the sentiment from the arguments with two separate tools. Alchemy API 3 , the sentiment analysis feature of IBM's Semantic Text Analysis API, returns a polarity label (positive, negative or neutral) and a polarity score between -1 (totally negative) and 1 (totally positive). The UNIBA system [START_REF] Basile | Uniba at evalita 2014-sentipolc task: Predicting tweet sentiment polarity combining micro-blogging, lexicon and semantic features[END_REF], one of the most successful participants in the Sentipolc task at Evalita 2014 [START_REF] Basile | Uniba at evalita 2014-sentipolc task: Predicting tweet sentiment polarity combining micro-blogging, lexicon and semantic features[END_REF], returns a subjectivity label (subjective or objective) and a polarity label (positive, negative, neutral or mixed).

Topic model We train a domain-independent topic model for Italian and compute, for each argument, its representing vector in the topic space. The 300-dimensional topic model is created with Gensim 4 using the ItWaC corpus [START_REF] Baroni | The wacky wide web: A collection of very large linguistically processed webcrawled corpora. language resources and evaluation[END_REF]. We use the vector components as features directly, i.e., each comment has 300 topic-based features.

Evaluation

The goal of the evaluation is twofold: i) to compute the performance of several machine learning methods and compare them with respect to some baselines, and ii) to investigate the importance of each group of features through an ablation test.

Data

We test our approach on the CorEA corpus [START_REF] Celli | Corea: Italian news corpus with emotions and agreement[END_REF], a collection of text from Italian news blogs. It contains 27 news articles, about 1,660 unique authors and more than 2,900 comments.

The corpus is annotated with emotions and, most interestingly for our work, the comments are annotated pair-wise with agreement information [START_REF] Celli | Multilevel annotation of agreement and disagreement in italian news blogs[END_REF]. We extracted such comment pairs for a total of 1,275 pairs: 682 disagreement, 106 neutral, 180 agreement (307 pairs are not classified, examples in Figure 2).

The CorEA dataset provides several information about each message. Beside the features described in Section 2.1, we also extract the following dataset-dependent features: the set of manually annotated topics, the news category of the article, the count of replies to the message, the count of message likes, the participant's activity score, the participant's interests, the participant's page views, the participant's total comments, the participant's total shares, the participant's likes received, and the overall emotion declared by the participant after reading the articles.

4 https://radimrehurek.com/gensim/

System setup

We exploit two kinds of learning algorithms: 1) different configurations of SVM based on linear kernel (SV M lin ), degree-2 polynomial kernel (SV M poly ), and RBF kernel (SV M rbf ); 2) Random Forest (RF ).

The baseline method always predicts the most frequent class, in this case "attack". Moreover, we test the two simple sentiment analysis systems already described in 2.1, SA alchemy and SA uniba . In particular, these systems exploit the result of the sentiment analysis in terms of polarity (positive, negative, or neutral) for predicting the relation between two arguments: if two arguments have the neutral polarity, they are tagged as neutral, while they are tagged as "support" in case they have the same polarity, otherwise the "attack" class is predicted. The system is implemented in JAVA relying on the Weka tool [START_REF] Hall | The weka data mining software: An update[END_REF]. All the experiments are performed by adopting the 10-folds cross-validation. For all the learning methods, we adopt the default Weka parameters since the goal of our work is not to optimize the classification performance but to provide a features study.

Results

Table 1 reports on the best results obtained by each method. Regarding RF the best result is obtained using 10 trees, while for SV M we optimize only the C parameter using default values for the other ones. The best C value for SV M lin is 1, 2 in all the other settings.

Each one of the supervised systems performs better than the baseline. The good performance of the linear kernel classifier is likely to be ascribed to the high number of features. The performance of Random Forest is also quite good, considering that only ten trees are employed. As can be seen from the results of ablation tests (see Table 2), the features that contribute the most

Relation Example

Attack "in certi paesi 100 sterline a settimana permettono di vivere come un pascià" "si ma in certi altri no..;-) la cifra mi sembra davvero esigua.." Support "Caro Renzi , hai visto com'è semplice restituire i soldi? Basta una firmetta... perchè non lo fai anche tu invece di promettere e promettere e promettere?" "Bisogna prendere atto che il movimento 5 stelle sta davvero restituendo i soldi agli Italiani. Questo è un fatto, tutto il resto sono chiacchere." Neutral "E le riforme?" "le riforme cominciano dl'atteggiamento dei parlamentari. con il cambiamento del mind-set . il punto di partenza."

Figure 2: Examples of relations between pairs of comments in CorEA.

to the argument classification task are the semantic features (i.e., embeddings) and the sentiment features. This confirms our hypothesis that sentiment is a key information for argument mining, and more specifically for the relation prediction task. The results also confirm that lexical and semantic features are useful for the task, as expected. Table 2 reports also the number of features (Feat.Size) and the F1 (F1-f) achieved by exploiting the respective feature in isolation. It is important to note that, despite the bad performance obtained by both embedding and sentiment features, their contribution in the overall performance is relevant. 4 Related Work [START_REF] Lippi | Argumentation mining: State of the art and emerging trends[END_REF] and [START_REF] Peldszus | From argument diagrams to argumentation mining in texts: A survey[END_REF] provide an overview about the argument mining research area. In particular, some approaches have been recently proposed to address the same task addressed in this paper, i.e. predicting relations between arguments, even if ours is the first effort for the Italian language. (Aharoni et al., 2014) assume that evidence is always associated with a claim, enabling the use of in-formation about the claim to predict the evidence.

The support relations are thus obtained by definition when predicting the evidence. [START_REF] Mochales | Argumentation mining[END_REF] have addressed the problem by parsing with a manually-built context-free grammar to predict relations between argument components. The grammar rules follow the typical rhetorical and structural patterns of sentences in juridical texts. This is a highly genre-specific approach, and its direct use in other genres would be unlikely to yield accurate results. [START_REF] Stab | Identifying argumentative discourse structures in persuasive essays[END_REF] instead employ a binary SVM classifier to predict relations in a claim/premise model. [START_REF] Biran | Identifying justifications in written dialogs by classifying text as argumentative[END_REF] apply the same method adopted for the detection of premises also for the prediction of relations between premises and claims. [START_REF] Wang | Improving agreement and disagreement identification in online discussions with a socially-tuned sentiment lexicon[END_REF] apply an isotonic Conditional Random Fields based sequential model to make predictions on sentence-or segment-level on discussions on Wikipedia Talk pages. Finally, [START_REF] Cabrio | A natural language bipolar argumentation approach to support users in online debate interactions †[END_REF] adopt Textual Entailment to infer whether a support or attack relation between two given arguments holds.

Conclusions

In this paper, we have presented a supervised approach for argument relation prediction for Italian, mainly relying on features including semantics and sentiment. We tested such approach on the CorEA corpus, extracted from user comments to online news. Our experimental results are good, and foster future research in the direction of including semantics as well as sentiment analysis in the argument mining pipeline. It will be also interesting, as future work, to refine the model in order to consider the full sequence of interactions between arguments.

Figure 1 :

 1 Figure 1: Example of debate structure.

Table 1

 1

			: Results
	System	P	R	F
	baseline	0.4964 0.7045 0.5824
	SA alchemy 0.3553 0.3616 0.3584
	SA uniba	0.2942 0.3286 0.3105
	SV M lin	0.6789 0.7169 0.6719
	RF	0.6607 0.7180 0.6491
	SV M poly	0.6609 0.7097 0.6486
	SV M rbf	0.6414 0.7076 0.6120

Table 2

 2

		: Ablation test
	Features	F1	∆% Feat.Size F1-f
	all	0.6719	-	220,499	-
	-lexical	0.6624 -1.42	140,443 0.66
	-syntactic	0.6702 -0.26	80,909 0.65
	-info	0.6691 -0.42	220,490 0.58
	-CorEA	0.6674 -0.68	220,218 0.64
	-embedding 0.6525 -2.89	220,399 0.59
	-overlap	0.6724 0.07	220,498 0.58
	-sentiment	0.6622 -1.45	220,491 0.58
	-topic	0.6673 -0.69	220,045 0.59

http://universaldependencies.org/it/ overview/introduction.html

The occurrences of the word "non"

http://www.alchemyapi.com/


Francesco Barbieri
email: francesco.barbieri@upf.edu

Valerio Basile
email: valerio.basile@inria.fr

Danilo Croce

Malvina Nissim
email: m.nissim@rug.nl

Nicole Novielli
email: nicole.novielli@uniba.it

Viviana Patti
email: patti@di.unito.it

Overview of the Evalita 2016 SENTIment POLarity Classification Task

The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification, polarity classification, and irony detection. In 2016 SENTIPOLC has been again the most participated EVALITA task with a total of 57 submitted runs from 13 different teams. We present the datasets -which includes an enriched annotation scheme for dealing with the impact on polarity of a figurative use of language -the evaluation methodology, and discuss results and participating systems.

Italiano. Descriviamo modalità e risultati della seconda edizione della campagna di valutazione di sistemi di sentiment analysis (SENTIment POLarity Classification Task), proposta nel contesto di "EVALITA 2016: Evaluation of NLP and Speech Tools for Italian". In SENTIPOLC è stata valutata la capacità dei sistemi di riconoscere diversi aspetti del sentiment espresso nei messaggi Twitter in lingua italiana, con un'articolazione in tre sottotask: subjectivity classification, polarity classification e irony detection. La campagna ha suscitato nuovamente grande interesse, con un totale di 57 run inviati da 13 gruppi di partecipanti.

Introduction

Sentiment classification on Twitter, namely detecting whether a tweet is polarised towards a positive or negative sentiment, is by now an established task. Such solid and growing interest is reflected in the fact that the Sentiment Analysis tasks at Se-mEval (where they constitute now a whole track) have attracted the highest number of participants in the last years [START_REF] Rosenthal | SemEval-2014 Task 9: Sentiment Analysis in Twitter[END_REF][START_REF] Rosenthal | SemEval-2015 Task 10: Sentiment Analysis in Twitter[END_REF][START_REF] Nakov | Semeval-2016 task 4: Sentiment analysis in twitter[END_REF], and so it has been for the latest Evalita campaign, where a sentiment classification task (SENTIPOLC 2014) was introduced for the first time [START_REF] Valerio Basile | Overview of the Evalita 2014 SENTIment POLarity Classification Task[END_REF].

In addition to detecting the polarity of a tweet, it is also deemed important to detect whether a tweet is subjective or is merely reporting some fact, and whether some form of figurative mechanism, chiefly irony, is also present. Subjectivity, polarity, and irony detection form the three tasks of the SENTIPOLC 2016 campaign, which is a rerun of SENTIPOLC 2014.

Innovations with respect to SENTIPOLC 2014 While the three tasks are the same as those organised within SENTIPOLC 2014, we want to highlight the innovations that we have included in this year's edition. First, we have introduced two new annotation fields which express literal polarity, to provide insights into the mechanisms behind polarity shifts in the presence of figurative usage. Second, the test data is still drawn from Twitter, but it is composed of a portion of random tweets and a portion of tweets selected via keywords, which do not exactly match the selection procedure that led to the creation of the training set. This was intentionally done to observe the portability of supervised systems, in line with what observed in [START_REF] Basile | Deep tweets: from entity linking to sentiment analysis[END_REF]. Third, a portion of the data was annotated via Crowdflower rather than by experts. This has led to several observations on the quality of the data, and on the theoretical description of the task itself. Fourth, a portion of the test data overlaps with the test data from three other tasks at Evalita 2016, namely PoST-WITA [START_REF] Bosco | Overview of the EVALITA 2016 Part Of Speech on TWitter for ITAlian Task[END_REF], NEEL-IT (Basile et al., 2016a), and FactA [START_REF] Minard | The EVALITA 2016 Event Factuality Annotation Task (FactA)[END_REF]. This was meant to produce a layered annotated dataset where end-to-end systems that address a variety of tasks can be fully developed and tested.

Task description

As in SENTIPOLC 2014, we have three tasks.

Task 1: Subjectivity Classification: a system must decide whether a given message is subjective or objective [START_REF] Bruce | Recognizing Subjectivity: A Case Study in Manual Tagging[END_REF][START_REF] Pang | Opinion Mining and Sentiment Analysis[END_REF].

Task 2: Polarity Classification: a system must decide whether a given message is of positive, negative, neutral or mixed sentiment. Differently from most SA tasks (chiefly the Semeval tasks) and in accordance with [START_REF] Valerio Basile | Overview of the Evalita 2014 SENTIment POLarity Classification Task[END_REF], in our data positive and negative polarities are not mutually exclusive and each is annotated as a binary category. A tweet can thus be at the same time positive and negative, yielding a mixed polarity, or also neither positive nor negative, meaning it is a subjective statement with neutral polarity. 1 Section 3 provides further explanation and examples.

Task 3: Irony Detection: a system must decide whether a given message is ironic or not. Twitter communications include a high percentage of ironic messages [START_REF] Davidov | Semi-supervised recognition of sarcastic sentences in Twitter and Amazon[END_REF][START_REF] Hao | An ironic fist in a velvet glove: Creative mis-representation in the construction of ironic similes[END_REF][START_REF] González-Ibáñez | Identifying sarcasm in twitter: A closer look[END_REF][START_REF] Reyes | A multidimensional approach for detecting irony in twitter[END_REF][START_REF] Reyes | On the difficulty of automatically detecting irony: Beyond a simple case of negation[END_REF], and platforms monitoring the sentiment in Twitter messages experienced the phenomenon of wrong polarity classification in ironic messages [START_REF] Bosco | Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT[END_REF][START_REF] Ghosh | Semeval-2015 task 11: Sentiment analysis of figurative language in Twitter[END_REF]. Indeed, ironic devices in a text can work as unexpected "polarity reversers" (one says something "good" to mean something "bad"), thus undermining systems' accuracy. In this sense, though not including a specific task on its detection, we have added an annotation layer of literal polarity (see Section 3.2) which could be potentially used by systems, and also allows us to observe patterns of irony.

The three tasks are meant to be independent. For example, a team could take part in the polarity classification task without tackling Task 1. 1 In accordance with [START_REF] Wiebe | Annotating expressions of opinions and emotions in language[END_REF].

Development and Test Data

Data released for the shared task comes from different datasets. We re-used the whole SEN-TIPOLC 2014 dataset, and also added new tweets derived from different datasets previously developed for Italian. The dataset composition has been designed in cooperation with other Evalita 2016 tasks, in particular the Named Entity rEcognition and Linking in Italian Tweets shared task (NEEL-IT, Basile et al. (2016a)). The multiple layers of annotation are intended as a first step towards the long-term goal of enabling participants to develop end-to-end systems from entity linking to entitybased sentiment analysis [START_REF] Basile | Deep tweets: from entity linking to sentiment analysis[END_REF]. A portion of the data overlaps with data from NEEL-IT (Basile et al., 2016a), PoSTWITA (Bosco et al., 2016) and FacTA [START_REF] Minard | The EVALITA 2016 Event Factuality Annotation Task (FactA)[END_REF]. See (Basile et al., 2016b) for details.

Corpora Description

Both training and test data developed for the 2014 edition of the shared task were included as training data in the 2016 release. Summarizing, the data that we are using for this shared task is a collection of tweets which is partially derived from two existing corpora, namely Sentipolc 2014 (TW-SENTIPOLC14, 6421 tweets) [START_REF] Valerio Basile | Overview of the Evalita 2014 SENTIment POLarity Classification Task[END_REF], and TWitterBuonaScuola (TW-BS) [START_REF] Stranisci | Annotating sentiment and irony in the online italian political debate on #labuonascuola[END_REF], from which we selected 1500 tweets. Furthermore, two new sets have been annotated from scratch following the SEN-TIPOLC 2016 annotation scheme: the first one consists of a set of 1500 tweets selected from the TWITA 2015 collection (TW-TWITA15, [START_REF] Basile | Sentiment analysis on Italian tweets[END_REF]), the second one consists of 1000 (reduced to 989 after eliminating malformed tweets) tweets collected in the context of the NEEL-IT shared task (TW-NEELIT, Basile et al. (2016a)). The subsets of data extracted from existing corpora (TW-SENTIPOLC14 and TW-BS) have been revised according to the new annotation guidelines specifically devised for this task (see Section 3.3 for details).

Tweets in the datasets are marked with a "topic" tag. The training data includes both a political collection of tweets and a generic collection of tweets. The former has been extracted exploiting specific keywords and hashtags marking political topics (topic = 1 in the dataset), while the latter is composed of random tweets on any topic (topic = 0). The test material includes tweets from the TW-BS corpus, that were extracted with a specific socio-political topic (via hashtags and keywords related to #labuonascuola, different from the ones used to collect the training material). To mark the fact that such tweets focus on a different topic they have been marked with topic = 2. While SEN-TIPOLC does not include any task which takes the "topic" information into account, we release it in case participants want to make use of it.

Annotation Scheme

Six fields contain values related to manual annotation are: subj, opos, oneg, iro, lpos, lneg.

The annotation scheme applied in SEN-TIPOLC 2014 has been enriched with two new fields, lpos and lneg, which encode the literal positive and negative polarity of tweets, respectively. Even if SENTIPOLC does not include any task which involves the actual classification of literal polarity, this information is provided to enable participants to reason about the possible polarity inversion due to the use of figurative language in ironic tweets. Indeed, in the presence of a figurative reading, the literal polarity of a tweet might differ from the intended overall polarity of the text (expressed by opos and oneg). Please note the following issues about our annotation scheme:

• An objective tweet will not have any polarity nor irony, thus if subj = 0, then opos = 0, oneg = 0, iro = 0, lpos = 0, and lneg = 0 . • A subjective, non ironic, tweet can exhibit at the same time overall positive and negative polarity (mixed polarity), thus opos = 1 and oneg = 1 can co-exist. Mixed literal polarity might also be observed, so that lpos = 1 and lneg = 1 can co-exist, and this is true for both non-ironic and ironic tweets. • A subjective, non ironic, tweet can exhibit no specific polarity and be neutral but with a subjective flavor, thus subj = 1 and opos = 0, oneg = 0. Neutral literal polarity might also be observed, so that lpos = 0 and lneg = 0 is a possible combination; this is true for both non-ironic and ironic tweets. • An ironic tweet is always subjective and it must have one defined polarity, so that iro = 1 cannot be combined with opos and oneg having the same value. However, mixed or neutral literal polarity could be observed for ironic tweets. Therefore, iro = 1, lpos = 0, and lneg = 0 can co-exist, as well as iro = 1, lpos = 1, and lneg = 1.

• For subjective tweets without irony (iro = 0), the overall (opos and oneg) and the literal (lpos and lneg) polarities are always annotated consistently, i.e. opos = lpos and oneg = lneg. Note that in such cases the literal polarity is implied automatically from the overall polarity and not annotated manually. The manual annotation of literal polarity only concerns tweets with iro = 1.

Table 1 summarises the allowed combinations.

Annotation procedure

Annotations for data from existing corpora (TW-BS and TW-SENTIPOLC14) have been revised and completed by exploiting an annotation procedure which involved a group of six expert annotators, in order to make them compliant to the SENTIPOLC 2016 annotation scheme. Data from NEEL-IT and TWITA15 was annotated from scratch using CrowdFlower. Both training and test data included a mixture of data annotated by experts and crowd. In particular, the whole TW-SENTIPOLC14 has been included in the development data release, while TW-BS was included in the test data release. Moreover, a set of 500 tweets from crowdsourced data was included in the test set, after a manual check and re-assessment (see below: Crowdsourced data: consolidation of annotations). This set contains the 300 tweets used as test data in the PoSTWITA, NEEL-IT-it and FactA EVALITA 2016 shared tasks. TW-SENTIPOLC14 Data from the previous evaluation campaign didn't include any distinction between literal and overall polarity. Therefore, the old tags pos and neg were automatically mapped into the new labels opos and oneg, respectively, which indicate overall polarity. Then, we had to extend the annotation to provide labels for positive and negative literal polarity. In case of tweets without irony, literal polarity values were implied from the overall polarity. For ironic tweets, instead, i.e. iro = 1 (806 tweets), we resorted to manual annotation: for each tweet, two independent annotations have been provided for the literal polarity dimension. The inter-annotator agreement at this stage was κ = 0.538. In a second round, a third independent annotation was provided to solve the disagreement. The final label was assigned by majority vote on each field independently. With three annotators, this procedure ensures an unambiguous result for every tweet. TW-BS The TW-BS section of the dataset had been previously annotated for polarity and irony 2 . The original TW-BS annotation scheme, however, did not provide any separate annotation for overall and literal polarity. The tags POS, NEG, MIXED and NONE, HUMPOS, HUMNEG in TW-BS were automatically mapped in the following values for the SENTIPOLC's subj, opos, oneg, iro, lpos and lneg annotation fields: POS ⇒ 110010; NEG ⇒ 101001; MIXED ⇒ 111011; NONE ⇒ 000000 3 ; HUMPOS ⇒ 1101??; HUM-NEG ⇒ 1011??. For the last two cases, i.e. where iro=1, the same manual annotation procedure 2 For the annotation process and inter-annotator agreement see [START_REF] Stranisci | Annotating sentiment and irony in the online italian political debate on #labuonascuola[END_REF] 3 Two independent annotators reconsidered the set of tweets tagged by NONE in order to distinguish the few cases of subjective, neutral, not-ironic tweets, i.e. 100000, as the original TW-BS scheme did not allow such finer distinction. The inter-annotator agreement on this task was measured as κ = 0.841 and a third independent annotation was used to solve the few cases of disagreement. described above was applied to obtain literal polarity values: two independent annotations were provided (inter-annotator agreement κ = 0.605), and a third annotation was added in a second round in cases of disagreement. Just as with the TW-SENTIPOLC14 set, the final label assignment was done by majority vote on each field.

TW-TWITA15 and TW-NEEL-IT For these new datasets, all fields were annotated from scratch using CrowdFlower (CF)4 , a crowdsourcing platform which has also been recently used for a similar annotation task [START_REF] Nakov | Semeval-2016 task 4: Sentiment analysis in twitter[END_REF]. CF enables quality control of the annotations across a number of dimensions, also by employing test questions to find and exclude unreliable annotators. We gave the users a series of guidelines in Italian, including a list of examples of tweets and their annotation according to the SENTIPOLC scheme. The guidelines also contained an explanation of the rules we followed for the annotation of the rest of the dataset, although in practice these constraints were not enforced in the CF interface. As requested by the platform, we provided a restricted set of "correct" answers to test the reliability of the users. This step proved to be challenging, since in many cases the annotation of at least one dimension is not clear cut. We required to collect at least three independent judgments for each tweet. The total cost of the crowdsourcing has been 55 USD and we collected 9517 judgments in total from 65 workers. We adopted the default CF settings for assigning the majority label (relative majority). The CF reported average confidence (i.e., inter-rater agreement) is 0.79 for subjectivity, 0.89 for positive polarity (0.90 for literal positivity), 0.91 for negative polarity (0.93 for literal negativity) and 0.92 for irony. While such scores appear high, they are skewed towards the over-assignment of the "0" label for basically all of classes (see below for further comments on this). Percentage agreement on the assignment of "1" is much lower (ranging from 0.70 to 0.77). 5 On the basis of such observations and on a first analysis of the resulting combinations, we operated a few revisions on the crowd-collected data. Crowdsourced data: consolidation of annotations Despite having provided the workers with guidelines, we identified a few cases of value combinations that were not allowed in our annotation scheme, e.g., ironic or polarised tweets (positive, negative or mixed) which were not marked as subjective. We automatically fixed the annotation for such cases, in order to release datasets of only tweets annotated with labels consistent with the SENTIPOLC's annotation scheme. 6 Moreover, we applied a further manual check of crowdsourced data stimulated by the following observations. When comparing the distributions of values (0,1) for each label in both training and crowdsourced test data, we observed, as mentioned above, that while the assignment of 1s constituted from 28 to 40% of all assignments for the opos/pos/ oneg/neg labels, and about 68% for the subjectivity label, figures were much lower for the crowdsourced data, with percentages as low as 5 This would be taken into account if using Kappa, which is however an unsuitable measure in this context due to the varying number of annotators per instance. 6 In particular, for CF data we applied two automatic transformations for restoring consistency of configurations of annotated values in cases where we observed a violation of the scheme: when at least a value 1 is present in the fields opos, oneg, iro, lpos, or lneg, we set the field subj accordingly: subj=0 ⇒ subj=1; when iro=0, the literal polarity value is overwritten by the overall polarity value. 6 (neg), 9 (pos), 11 (oneg), and 17 (opos), and under 50% for subj. 7 This could be an indication of a more conservative interpretation of sentiment on the part of the crowd (note that 0 is also the default value), possibly also due to too few examples in the guidelines, and in any case to the intrinsic subjectivity of the task. On such basis, we decided to add two more expert annotations to the crowdannotated test-set, and take the majority vote from crowd, expert1, and expert2. This does not erase the contribution of the crowd, but hopefully maximises consistency with the guidelines in order to provide a solid evaluation benchmark for this task.

Format and Distribution

We provided participants we a single development set, which consists of a collection of 7,410 tweets, with IDs and annotations concerning all three SENTIPOLC's subtasks: subjectivity classification (subj), polarity classification (opos,oneg) and irony detection (iro).

Including the two additional fields with respect to SENTIPOLC 2014, namely lpos and lneg, the final data format of the distribution is as follows: "id", "subj, "opos", "oneg", "iro", "lpos", "lneg", "top", "text".

The development data includes for each tweet the manual annotation for the subj, opos, oneg, iro, lpos and lneg fields, according to the format explained above. Instead, the blind version of the test data, which consists of 2000 tweets, only contains values for the idtwitter and text fields. In other words, the development data contains the six columns manually annotated, while the test data will contain values only in the first (idtwitter) and last two columns (top and text). The literal polarity might be predicted and used by participants to provide the final classification of the items in the test set, however this should be specified in the submission phase. The distribution of combinations in both development and test data is given in Table 2.

Evaluation

Task1: subjectivity classification. Systems are evaluated on the assignment of a 0 or 1 value to the subjectivity field. A response is considered plainly correct or wrong when compared to the gold standard annotation. We compute precision (p), recall (r) and F-score (F) for each class (subj,obj):

p class = #correct class #assigned class r class = #correct class #total class F class = 2 p class r class p class + r class
The overall F-score will be the average of the Fscores for subjective and objective classes.

Task2: polarity classification. Our coding system allows for four combinations of opos and oneg values: 10 (positive polarity), 01 (negative polarity), 11 (mixed polarity), 00 (no polarity). Accordingly, we evaluate positive and negative polarity independently by computing precision, recall and F-score for both classes (0 and 1): The F-score for the two polarity classes is the average of the F-scores of the respective pairs:

F pos = (F pos 0 + F pos 1 ) 2 F neg = (F neg 0 + F neg 1 ) 2
Finally, the overall F-score for Task 2 is given by the average of the F-scores of the two polarities.

Task3: irony detection. Systems are evaluated on their assignment of a 0 or 1 value to the irony field. A response is considered fully correct or wrong when compared to the gold standard annotation. We measure precision, recall and F-score for each class (ironic,non-ironic), similarly to the Task1, but with different targeted classes. The overall F-score will be the average of the F-scores for ironic and non-ironic classes.

Informal evaluation of literal polarity classification. Our coding system allows for four combinations of positive (lpos) and negative (lneg) values for literal polarity, namely: 10: positive literal polarity; 01: negative literal polarity; 11: mixed literal polarity; 00: no polarity.

SENTIPOLC does not include any task that explicitly takes into account the evaluation of literal polarity classification. However, participants could find it useful in developing their system, and might learn to predict it. Therefore, they could choose to submit also this information to receive an informal evaluation of the performance on these two fields, following the same evaluation criteria adopted for Task 2. The performance on the literal polarity classification will not affect in any way the final ranks for the three SENTIPOLC tasks.

Participants and Results

A total of 13 teams from 6 different countries participated in at least one of the three tasks of SENTIPOLC. Table 3 provides an overview of the teams, their affiliation, their country (C) and the tasks they took part in. Almost all teams participated to both subjectivity and polarity classification subtasks. Each team had to submit at least a constrained run. Furthermore, teams were allowed to submit up to four runs (2 constrained and 2 unconstrained) in case they implemented different systems. Overall we have 19, 26, 12 submitted runs for the subjectivity, polarity, and irony detection tasks, respectively. In particular, three teams (UniPI, Unitor and tweet2check) participated with both a constrained and an unconstrained runs on the both the subjectivity and polarity subtasks. Unconstrained runs were submitted to the polarity subtask only by IntIntUniba.SentiPy and INGEOTEC.B4MSA. Differently from SEN-TIPOLC 2014, unconstrained systems performed better than constrained ones, with the only exception of UniPI, whose constrained system ranked first for the polarity classification subtask.

We produced a single-ranking table for each subtask, where unconstrained runs are properly marked. Notice that we only use the final F-score for global scoring and ranking. However, systems that are ranked midway might have excelled in precision for a given class or scored very bad in recall for another. 8 For each task, we ran a majority class baseline to set a lower-bound for performance. In the tables it is always reported as Baseline.

Task1: subjectivity classification

Table 4 shows results for the subjectivity classification task, which attracted 19 total submissions from 10 different teams. The highest F-score is achieved by Unitor at 0.7444, which is also the best unconstrained performance. Among the constrained systems, the best F-score is achieved by samskara with F = 0.7184. All participating systems show an improvement over the baseline.

Task2: polarity classification

Table 5 shows results for polarity classification, the most popular subtask with 26 submissions from 12 teams. The highest F-score is achieved by UniPi at 0.6638, which is also the best score among the constrained runs. As for unconstrained runs, the best performance is achieved by Unitor with F = 0.6620. All participating systems show an improvement over the baseline. 9

8 Detailed scores for all classes and tasks are available at http://www.di.unito.it/ ˜tutreeb/ sentipolc-evalita16/index.html 9 After the deadline, SwissCheese and tweet2check reported about a conversion error from their internal format to the official one. The resubmitted amended runs are shown in the table (marked by the * symbol), but the official ranking was not revised.

Table 4: Task 1: F-scores for constrained ".c" and unconstrained runs ".u". After the deadline, two teams reported about a conversion error from their internal format to the official one. The resubmitted amended runs are marked with *.

Tweet representation schemas. Almost all teams adopted (i) traditional manual feature engineering or (ii) distributional models (i.e. Word embeddings) to represent tweets. The teams adopting the strategy (i) make use of traditional feature modeling, as presented in SENTIPOLC 2014, using specific features that encode word-based, syntactic and semantic (mostly lexicon-based) features. Exploitation of additional data for training. Some teams submitted unconstrained results, as they used additional Twitter annotated data for training their systems. In particular, UniPI used a silver standard corpus made of more than 1M tweets to pre-train the CNN; this corpus is annotated using a polarity lexicon and specific polarised words. Also Unitor used external tweets to pre-train their CNN. This corpus is made of the contexts of the tweets populating the training material and automatically annotated using the classifier trained only over the training material, in a semi-supervised fashion. Moreover, Unitor used distant supervision to label a set of tweets used for the acquisition of their so-called Distribution Polarity Lexicon. Distant supervision is also adopted by INGEOTEC to extend the training material for the their SVM classifier.

External Resources. The majority of teams used external resources, such as lexicons specific for Sentiment Analysis tasks. Some teams used already existing lexicons, such as Samskara, Ital-iaNLP, CoLingLab, or CoMoDi, while others created their own task specific resources, such as Unitor, IRADABE, CoLingLab.

Issues about the interdependency of tasks.

Among the systems participating in more than one task, SwissCheese and Unitor designed systems that exploit the interdependency of specific subtasks. In particular, SwissCheese trained one CNN for all the tasks simultaneously, by joining the labels. The results of their experiments indicate that the multi-task CNN outperforms the single-task CNN. Unitor made the training step dependent on the subtask, e.g. considering only subjective tweets when training the Polarity Classifier. However it is difficult to assess the contribution of cross-task information based only on the experimental results obtained by the single teams.

Irony detection.

As also observed at SEN-TIPOLC 2014, irony detection appears truly challenging, as even the best performing system submitted by Tweet2Check (F = 0.5412) shows a low recall of 0.1710. We also observe that the performances of the supervised system developed by Tweet2Check and CoMoDi's rule-based approach, specifically tailored for irony detection, are very similar (Table 6).

While results seem to suggest that irony detection is the most difficult task, its complexity does not depend (only) on the inner structure of irony, but also on unbalanced data distribution (1 out of 7 examples is ironic in the training set). The classifiers are thus biased towards the non-irony class, and tend to retrieve all the non-ironic examples (high recall in the class non-irony) instead of actually modelling irony. If we measure the number of correctly predicted examples instead of the average of the two classes, the systems perform well (micro F1 of best system is 0.82).

Moreover, performance for irony detection drops significantly compared to SENTIPOLC 2014. An explanation for this could be that unlike SENTIPOLC 2014, at this edition the topics in the train and in the test sets are different, and it has been shown that systems might be modelling topic rather than irony [START_REF] Barbieri | How Topic Biases Your Results? A Case Study of Sentiment Analysis and Irony Detection in Italian[END_REF]. This evidence suggests that examples are probably not sufficient to generalise over the structure of ironic tweets. We plan to run further experiments on this issue, including a larger and more balanced dataset of ironic tweets in future campaigns.

Closing Remarks

All systems, except CoMoDI, exploited machine learning techniques in a supervised setting. Two main strategies emerged. One involves using linguistically principled approaches to represent tweets and provide the learning framework with valuable information to converge to good results. The other exploits state-of-the-art learning frameworks in combination with word embedding methods over large-scale corpora of tweets. On balance, the last approach achieved better results in the final ranks. However, with F-scores of 0.744 (unconstrained) and 0.7184 (constrained) in subjectivity recognition and 0.6638 (constrained) and 0.6620 (unconstrained) in polarity recognition, we are still far from having solved sentiment analysis on Twitter. For the future, we envisage the definition of novel approaches, for example by combining neural network-based learning with a linguistic-aware choice of features.

Besides modelling choices, data also matters. At this campaign we intentionally designed a test set with a sampling procedure that was close but not identical to that adopted for the training set (focusing again on political debates but on a different topic), so as to have a means to test the generalisation power of the systems [START_REF] Basile | Deep tweets: from entity linking to sentiment analysis[END_REF]. A couple of teams indeed reported substantial drops from the development to the official test set (e.g. IRADABE), and we plan to further investigate this aspect in future work. Overall, results confirm that sentiment analysis of micro-blogging is challenging, mostly due to the subjective nature of the phenomenon, and it's reflected in the interannotator agreement (Section 3.3). Crowdsourced data for this task also proved to be not entirely reliable, but this requires a finer-grained analysis on the collected data, and further experiments including a stricter implementation of the guidelines.

Although evaluated over different data, we see that this year's best systems show better, albeit comparable, performance for subjectivity with respect to 2014's systems, and outperform them for polarity (if we consider late submissions). For a proper evaluation across the various editions, we propose the use of a progress set for the next edition, as already done in the SemEval campaign.

Table 1 :

 1 Combinations of values allowed by our annotation scheme Non

	subj opos oneg iro lpos lneg	description and explanatory tweet in Italian
	0	0	0	0	0	0	objective l'articolo di Roberto Ciccarelli dal manifesto di oggi http://fb.me/1BQVy5WAk
	1	0	0	0	0	0	subjective with neutral polarity and no irony Primo passaggio alla #strabrollo ma secondo me non era un iscritto
							subjective with positive polarity and no irony
	1	1	0	0	1	0	splendida foto di Fabrizio, pluri cliccata nei siti internazionali di Photo Natura http:
							//t.co/GWoZqbxAuS
							subjective with negative polarity and no irony
	1	0	1	0	0	1	Monti, ripensaci: l'inutile Torino-Lione inguaia l'Italia: Tav, appello a Mario Monti
							da Mercalli, Cicconi, Pont... http://t.co/3CazKS7Y
							subjective with both positive and negative polarity (mixed polarity) and no irony
	1	1	1	0	1	1	Dati negativi da Confindustria che spera nel nuovo governo Monti. Castiglione:
							"Avanti con le riforme" http://t.co/kIKnbFY7
							subjective with positive polarity, and an ironic twist
	1	1	0	1	1	0	Questo governo Monti dei paschi di Siena sta cominciando a carburare; speriamo
							bene...
	1	1	0	1	0	1	subjective with positive polarity, an ironic twist, and negative literal polarity Non riesco a trovare nani e ballerine nel governo Monti. Ci deve essere un errore! :)
							subjective with negative polarity, and an ironic twist
	1	0	1	1	0	1	Calderoli: Governo Monti? Banda Bassotti ..infatti loro erano quelli della Magliana..
							#FullMonti #fuoritutti #piazzapulita
							subjective with negative polarity, an ironic twist, and positive literal polarity
	1	0	1	1	1	0	Ho molta fiducia nel nuovo Governo Monti. Più o meno la stessa che ripongo in mia
							madre che tenta di inviare un'email.
							subjective with positive polarity, an ironic twist, and neutral literal polarity
	1	1	0	1	0	0	Il vecchio governo paragonato al governo #monti sembra il cast di un film di lino banfi
							e Renzo montagnani rispetto ad uno di scorsese
	1	0	1	1	0	0	subjective with negative polarity, an ironic twist, and neutral literal polarity arriva Mario #Monti: pronti a mettere tutti il grembiulino?
							subjective with positive polarity, an ironic twist, and mixed literal polarity
	1	1	0	1	1	1
	1	0	1	1	1	1	subjective with negative

aspettare che il Governo Monti prenda anche i tuoi regali di Natale... Corri da noi, e potrai trovare IDEE REGALO a partire da 10e... polarity, an ironic twist, and mixed literal polarity applauso freddissimo al Senato per Mario Monti. Ottimo.

Table 2 :

 2 Distribution of value combinations

	combination subj opos oneg iro	lpos lneg	dev	test
	0	0	0	0	0	0	2,312	695
	1	0	0	0	0	0	504	219
	1	0	1	0	0	1	1,798	520
	1	0	1	1	0	0	210	73
	1	0	1	1	0	1	225	53
	1	0	1	1	1	0	239	66
	1	0	1	1	1	1	71	22
	1	1	0	0	1	0	1,488	295
	1	1	0	1	0	0	29	3
	1	1	0	1	0	1	22	4
	1	1	0	1	1	0	62	8
	1	1	0	1	1	1	10	6
	1	1	1	0	1	1	440	36
			total				7,410 2,000

Table 3 :

 3 Teams participating to SENTIPOLC 2016

	team	institution	C	tasks
	ADAPT	Adapt Centre	IE	T1,T2,T3
	CoLingLab CoLingLab University of Pisa	IT	T2
	CoMoDI	FICLIT University of Bologna	IT	T3
	INGEOTEC CentroGEO/INFOTEC CONACyT	MX T1,T2
	IntIntUniba University of Bari	IT	T2
	IRADABE Univer. Pol. de Valencia, Université de Paris	ES,FR T1,T2,T3
	ItaliaNLP	ItaliaNLP Lab ILC (CNR)	IT	T1,T2,T3
	samskara	LARI Lab, ILC CNR	IT	T1,T2
	SwissCheese Zurich University of Applied Sciences	CH	T1,T2,T3
	tweet2check Finsa s.p.a.	IT	T1,T2,T3
	UniBO	University of Bologna	IT	T1,T2
	UniPI	University of Pisa	IT	T1,T2
	Unitor	University of Roma Tor Vergata	IT	T1,T2,T3

Table 6 :

 6 Task 3: F-scores for constrained ".c" and unconstrained runs ".u". Amended runs are marked with *.

	System	Non-Iro	Iro	F
	tweet2check16.c	0.9115	0.1710 0.5412
	CoMoDI.c	0.8993	0.1509 0.5251
	tweet2check14.c	0.9166	0.1159 0.5162
	IRADABE.2.c	0.9241	0.1026 0.5133
	ItaliaNLP.1.c	0.9359	0.0625 0.4992
	ADAPT.c	0.8042	0.1879 0.4961
	IRADABE.1.c	0.9259	0.0484 0.4872
	Unitor.2.u	0.9372	0.0248 0.4810
	Unitor.c	0.9358	0.0163 0.4761
	Unitor.1.u	0.9373	0.0084 0.4728
	ItaliaNLP.2.c	0.9367	0.0083 0.4725
	Baseline	0.9376	0.000	0.4688
	*SwissCheese.c late 0.9355	0.1367 0.5361
	In addition, micro-blogging specific features such
	as emoticons and hashtags are also adopted, for
	example by ColingLab, INGEOTEC) or Co-
	MoDi. Deep learning methods adopted by some
	teams, such as UniPi and SwissCheese required
	to model individual tweets through geometrical
	representation of tweets, i.e. vectors. Words
	from individual tweets are represented through
	Word Embeddings, mostly derived by using the
	Word2Vec tool or similar approaches. Unitor ex-
	tends this representation with additional features
	derived from Distributional Polarity Lexicons. In
	addition, some teams (e.g. ColingLab) adopted
	Topic Models to represent tweets. Samskara also
	used feature modelling with a communicative and
	pragmatic value. CoMoDi is one of the few sys-
	tems that investigated irony-specific features.

http://www.crowdflower.com/

The annotation of the presence of irony shows less distance, with 12% in the training set

and 8% in the crowdannotated test set.

Task3: irony detection

Table 6 shows results for the irony detection task, which attracted 12 submissions from 7 teams. The highest F-score was achieved by tweet2check at 0.5412 (constrained run). The only unconstrained run was submitted by Unitor achieving 0.4810 as F-score. While all participating systems show an improvement over the baseline (F = 0.4688), many systems score very close to it, highlighting the complexity of the task.

Discussion

We compare the participating systems according to the following main dimensions: classification framework (approaches, algorithms, features), tweet representation strategy, exploitation of further Twitter annotated data for training, exploitation of available resources (e.g. sentiment lexicons, NLP tools, etc.), and issues about the interdependency of tasks in case of systems participating in several subtasks.

Since we did not receive details about the systems adopted by some participants, i.e., tweet2check, ADAPT and UniBO, we are not including them in the following discussion. We consider however tweet2check's results in the discussion regarding irony detection.

Approaches based on Convolutional Neural Most of the other teams adopted learning methods already investigated in SENTIPOLC 2014; in particular, Support Vector Machine (SVM) is the most adopted learning algorithm. The SVM is generally based over specific linguistic/semantic feature engineering, as discussed for example by ItaliaNLP, IRADABE, INGEOTEC or Col-ingLab. Other methods have been also used, as a Bayesian approach by samskara (achieving good results in polarity recognition) combined with linguistically motivated feature modelling. CoMoDi is the only participant that adopted a rule based approach in combination with a rich set of linguistic cues dedicated to irony detection.