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EVALITA. Evaluation of NLP and Speech Tools for Italian

Pierpaolo Basile
Franco Cutugno
Malvina Nissim
et al.

Part II: EVALITA 2016: Task overviews and participants reports

IRADABE2: Lexicon Merging and Positional Features for Sentiment Analysis in Italian

Davide Buscaldi et Delia Irazù Hernandez-Farias


This paper presents the participation of the IRADABE team to the SENTIPOLC 2016 task. This year we investigated the use of positional features together with the fusion of sentiment analysis resources with the aim to classify Italian tweets according to subjectivity, polarity and irony. Our approach uses as starting point our participation in the SENTIPOLC 2014 edition. For classification we adopted a supervised approach that takes advantage of support vector machines and neural networks.

Quest’articolo presenta il lavoro svolto dal team IRADABE per la partecipazione al task SENTIPOLC 2016. Il lavoro svolto include l’utilizzo di caratteristiche posizionali e la fusione di lessici specialistici, finalizzato alla classificazione di tweet in italiano, secondo la loro soggetività, polarità ed ironia. Il nostro approccio si basa sull’esperienza acquisita nel corso della partecipazione all’edizione 2014 di SENTIPOLC. Per la classificazione sono stati adottati dei metodi supervisionati come le macchine a supporto vettoriale e le reti neurali.

Texte intégral

This research work has been supported by the “Investissements d’Avenir” program ANR-10LABX-0083 (Labex EFL). The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazú Hernańdez Farías (Grant No. 218109/313683 CVU-369616).

1 Introduction

1Sentiment analysis (SA) related tasks have attracted the attention of many researchers during the last decade. Several approaches have been proposed in order to address SA. Most of them have in common the use of machine learning together with natural language processing techniques. Despite all those efforts there still many challenges left such as: multililngual sentiment analysis, i.e, to perform SA in languages different from English (Mohammad, 2016). This year for the second time a sentiment analysis task on Italian tweets has been organized at EvalIta, the Sentiment Polarity Classification (SENTIPOLC) task (Barbieri et al., 2016).

2In this paper we study the effect of positional features over the sentiment, irony and polarity classification tasks in the context of SENTIPOLC 2016 task. We propose a revised version of our IRADABE system (Hernandez-Farias et al., 2014), which participated with fairly good results in 2014. The novelties for this participation are not only in the positional features, but also in a new sentiment lexicon that was built combining and expanding the lexicons we used in 2014.

3The rest of the paper is structured as follows: in Section 2 we describe the steps we took to build an enhanced sentiment dictionary in Italian from existing English resources; in Section 3 we describe the new positional features of the IRADABE system.

2 Building a unified dictionary

4In sentiment analysis related tasks, there are several factors that can be considered in order to determine the polarity of a given piece of text. Overall, the presence of positive or negative words is used as a strong indicator of sentiment. Nowadays there are many sentiment analysis related resources that can be exploited to infer polarity from texts. Recently, this kind of lexicons has been proven to be effective for detecting irony in Twitter (Hernández Farías et al., 2016). Unfortunately, the majority of available resources are in English. A common practice to deal with the lack of resources in different languages is to automatically translate it from English.

5However, the language barrier is not the only drawback for these resources. Another issue is the limited coverage of certain resources. For instance, AFINN (Nielsen, 2011) includes only 2477 words in its English version, and the Hu-Liu lexicon (Hu and Liu, 2004) contains about 6800 words. We verified on the SENTIPOLC14 training set that the Hu-Liu lexicon provided a score for 63.1% of training sentences, while the coverage for AFINN was of 70.7%, indicating that the number of items in the lexicons is not proportional to the expected coverage; in other words, although AFINN is smaller, the words included are more frequently used than those listed in the Hu-Liu lexicon. The coverage provided by a hypothetical lexicon obtaining by the combination of the two resources would be 79.5%.

  • 1

6We observed also that in some cases these lexicons provide a score for a word but not for one of their synonyms: in the Hu-Liu lexicon, for instance, the word ‘repel’ is listed as a negative one, but ‘resist’, which is listed as one of its synonym in the Roget’s thesaurus1, is not. SentiWordNet (Baccianella et al., 2010) compensates some of the issues; its coverage is considerably higher than the previously named lexicons: 90.6% on the SENTIPOLC14 training set. Its scores are also assigned to synsets, and not words. However, it is not complete: we measured that a combination of SentiWordNet with AFINN and Hu-Liu would attain a coverage of 94.4% on the SENTIPOLC14 training set. Moreover, the problem of working with synsets is that it is necessary to carry out word sense disambiguation, which is a difficult task, particularly in the case of short sentences like tweets. For this reason, our translation of SentiWordNet into Italian (Hernandez-Farias et al., 2014) resulted in a word-based lexicon and not a synset-based one.

7Therefore, we built a sentiment lexicon which was aimed to provide the highest possible coverage by merging existing resources and extending the scores to synonyms or quasi-synonyms. The sentiment lexicon was built following a three-step process:

  1. Create a unique set of opinion words from the AFINN, Hu-Liu and SentiWordNet lexicons, and merge the scores if multiple scores are available for the same word; the original English resources were previously translated into the Italian language for our participation in SENTIPOLC 2014;

  2. Extend the lexicon with the WordNet synonyms of words obtained in step 1;

  3. Extend the lexicon with pseudo-synonyms of words obtained in step 1 and 2, using word2vec for similarity. We denote them as “pseudo-synonyms” because the similarity according to word2vec doesn’t necessarily means that the words are synonyms, only that they usually share the same contexts.

8The scores at each step were calculated as follows: in step 1, the weight of a word is the average of the non-zero scores from the three lexicons. In step 2, the weight for a synonym is the same of the originating word. If the synonym is already in the lexicon, then we keep the most polarizing weight (if the scores have the same sign), or the sum of the weights (if the scores have opposed signs). For step 3 we previously built semantic vectors using word2vec (Mikolov et al., 2013) on the ItWaC2 corpus (Baroni et al., 2009). Then, we select for each word in the lexicon obtained at step 2 the 10 most similar pseudo-synonyms having a similarity score ≥ 0.6. If the related pseudosynonym already exists in the lexicon, its score is kept, otherwise it is added to the lexicon with a polarity resulting from the score of the original word multiplied by the similarity score of the pseudosynonym. We named the obtained resource the ‘Unified Italian Semantic Lexicon’, shortened as UnISeLex. It contains 31, 601 words. At step 1, the dictionary size was 12, 102; at step 2, after adding the synonyms, it contained 15, 412 words.

9In addition to this new resource, we exploited labMT-English words. It is a list (Dodds et al., 2011) composed of 10,000 words manually annotated with a happiness measure in a range between 0 up to 9. These words were collected from different resources such as Twitter, Google Books, music lyrics, and the New York Times (1987 to 2007).

3 Positional Features

10It is well known that in the context of opinion mining and summarization the position of opinion words is an important feature (Pang and Lee, 2008), (Taboada and Grieve, 2004). In reviews, users tend to summarize the judgment in the final sentence, after a comprehensive analysis of the various features of the item being reviewed (for instance, in a movie review, they would review the photography, the screenplay, the actor performance, and finally provide an overall judgment of the movie). Since SENTIPOLC is focused on tweets, whose length is limited to 140 characters, there is less room for a complex analysis and therefore it is not clear whether the position of sentiment words is important or not.

11In fact, we analyzed the training set and noticed that some words tend to appear in certain positions when the sentence is labelled with a class rather than the other one. For example, in the subjective sub-task, ‘non’ (not), ‘io’ (I), auxiliary verbs like ‘potere’ (can), ‘dovere’ (must) tend to occur mostly at the beginning of the sentence if the sentence is subjective. In the positive polarity subtask, words like ‘bello’ (beautiful), ‘piacere’ (like) and ‘amare’ (love) are more often observed at the beginning of the sentence if the tweet is positive.

12We therefore introduced a positional Bag-of-Words (BOW) weighting, where the weight of a word t is calculated as:

w(t) = 1 + pos(t)/len(s)

where pos(t) is the last observed position of the word in the sentence, and len(s) is the length of the sentence. For instance, in the sentence “I love apples in fall.”, w(love) = 1 + 1/5 = 1.2, since the word love is at position 1 in a sentence of 5 words.

13The Bag of Words was obtained by taking all the lemmatized forms w that appeared in the training corpus with a frequency greater than 5 and I(w) > 0.001, where I(w) is the informativeness of word w calculated as:

I(w) = p(w|c+) log(p(w|c+)) log(p(w|c-))

where p(w c+) and p(w c) are the probabilities of a word appearing in the tweets tagged with the positive or negative class, respectively. The result of this selection consisted in 943 words for the subj subtask, 831 for pos, 991 for neg and 1197 for iro.

14The results in Table 3 show a marginal improvement for the polarity and irony classes, while in subjectivity the system lost 2% in F-measure. This is probably due to the fact that the important words that tend to appear in the first part of the sentence may repeat later, providing a wrong score for the feature.

Table 1: F-measures for positional and standard BOW models trained on the train part of the dev set; results are calculated on the test part of the dev set.





pos. BOW





std. BOW





15With respect to the 2014 version of IRADABE, we introduced 3 more position-dependent features. Each tweet was divided into 3 sections, head, centre and tail. For each section, we consider the sum of the sentiment scores of the included words as a separate feature. Therefore, we have three features, named in Table 3.1 as headS, centreS and tailS.

Figure 1: Example of lexicon positional scores for the sentence “My phone is shattered as well my hopes and dreams”.

Figure 1: Example of lexicon positional scores for the sentence “My phone is shattered as well my hopes and dreams”.

3.1 Other features

16We renewed most of the features used for SENTIPOLC 2014, with the main difference that we are now using a single sentiment lexicon instead than 3. In IRADABE 2014 we grouped the features into two categories: Surface Features and Lexicon-based Features. We recall the ones appearing in Table 2, directing the reader to (Hernandez-Farias et al., 2014) for a more detailed description. The first group comprises features such as the presence of an URL address (http), the length of the tweet (length), a list of swearing words (taboo), and the ratio of uppercase characters (shout). Among the features extracted from dictionaries, we used the sum of polarity scores (polSum), the sum of only negative or positive scores (sum( ) and sum(+)), the number of negative scores (count( )) on UnISeLex, and the average and the standard deviation of scores on labMT (avglabMTand stdlabMT , respectively). Furthermore, to determine both polarity and irony, a subjectivity indicator (subj) feature was used; it is obtained by identifying first if a tweet is subjective or not. Finally, the mixed feature indicates is the tweet has mixed polarity or not.

Table 2: The 10 best features for each subtask in the training set.










4 Results and Discussion

  • 3 Further details on the datasets can be found in the task overview (Barbieri et al., 2016)

17We evaluated our approach on the dataset provided by the organizers of SENTIPOLC 2016. This dataset is composed by up to 10,000 tweets distributed in training set and test set. Both datasets contain tweets related to political and socio-political domains, as well as some generic tweets3.

18We experimented with different configurations for assessing subjectivity, polarity and irony. We sent two runs for evaluation purposes in SENTIPOLC-2016:

  • run 1. For assessign the subjectivity label a Tensorflow4 implementation of Deep Neural Ngetwork (DNN) was applied, with 2 hidden layers with 1024 and 512 states, respectively. Then, the polarity and irony labels were determined by exploiting a SVM5.

  • run 2. In this run, the bag-of-words were revised to remove words that may have a different polarity depending on the context (. Classification was carried out using a SVM (radial basis function kernel) for all subtasks, including subj.

19From the results, we can observe that the DNN obtained an excellent precision (more than 93%) in subj, but the recall was very low. This may indicate a problem due to the class not being balanced, or an overfitting problem with the DNN, which is plausible given the number of features. This may also be the reason for which the SVM performs better, because SVMs are less afflicted by the “curse of dimensionality”.

Table 3: Official results of our model on the test set.

run 1




















run 2




















5 Conclusions

20As future work, it could be interesting to exploit the labels for exact polarity as provided by the organizers. This kind of information could help in some way to identify the use of figurative language. Furthermore, we are planning to enrich IRADABE with other kinds of features that allow us to cover more subtle aspects of sentiment, such as emotions. The introduction of the “happiness score” provided by labMT was particularly useful, with the related features being critical in the subjectivity and polarity subtasks. This motivates us to look for dictionaries that may express different feelings than just the overall polarity of a word. We will also need to verify the effectiveness of the resource we produced automatically with respect to other hand-crafted dictionaries for the Italian language, such as Sentix (Basile and Nissim, 2013)

21We plan to use a more refined weighting scheme for the positional features, such as the locallyweighted bag-of-words or LOWBOW (Lebanon et al., 2007), although it would mean an increase of the feature space of at least 3 times (if we keep the head, centre, tail cuts), probably furtherly compromising the use of DNN for classification.

22About the utility of positional features, the current results are inconclusive, so we need to investigate further about how the positional scoring affects the results. On the other hand, the results show that the merged dictionary was a useful resource, with dictionary-based features representing 25% of the most discriminating features.


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3 Further details on the datasets can be found in the task overview (Barbieri et al., 2016)


5 As in IRADABE-2014 version, the subjectivity label influences the determination of both the polarity values and the presence of irony.

Table des illustrations

Titre Figure 1: Example of lexicon positional scores for the sentence “My phone is shattered as well my hopes and dreams”.
Fichier image/jpeg, 150k


LIPN, Université Paris 13 Villetaneuse, France -

Dipartimento di Informatica Università degli studi di Torino Turin, Italy - PRHLT group Universitat Politècnica de València Valencia, Spain -


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