Lacam&Int@UNIBA at the EVALITA 2016-SENTIPOLC Task
نویسندگان
چکیده
English. This paper describes our first experience of participation at the EVALITA challenge. We participated only to the SENTIPOLC Sentiment Polarity subtask and, to this purpose we tested two systems, both developed for a generic Text Categorization task, in the context of the sentiment analysis: SentimentWS and SentiPy. Both were developed according to the same pipeline, but using different feature sets and classification algorithms. The first system does not use any resource specifically developed for the sentiment analysis task. The second one, which had a slightly better performance in the polarity detection subtask, was enriched with an emoticon classifier in order to fit better the purpose of the challenge. Italiano. Questo articolo descrive la nostra prima esperienza di partecipazione ad EVALITA. Il nostro team ha partecipato solo al subtask inerente il riconoscimento della Sentiment Polarity, In questo contesot abbiamo testato due sistemi sviluppati genericamente per la Text Categorization applicandoli a questo specifico task: SentimentWS e SentiPy. Entrambi i sistemi usano la stessa pipeline ma con set di feature e algoritmi di classificazione differenti. Il primo sistema non usa alcuna risorsa specifiche per la sentment analysis, mentre il secondo, che si è classifcato meglio, pur mantendendo la sua genericità nella classificazione del testo, è stato arricchito con un classificatore per le emoticon per cercare di renderlo più adatto allo scopo della challenge.
منابع مشابه
Overview of the Evalita 2016 SENTIment POLarity Classification Task
English. 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...
متن کاملTweet2Check evaluation at Evalita Sentipolc 2016
English. In this paper we present our Tweet2Check tool, provide an analysis of the experimental results obtained by our tool at the Evalita Sentipolc 2016 evaluation, and compare its performance with the state-of-the-art tools that participated to the evaluation. In the experimental analysis, we show that Tweet2Check is: (i) the second classified for the irony task, at a distance of just 0.0068...
متن کاملIRADABE2: Lexicon Merging and Positional Features for Sentiment Analysis in Italian
English. 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 classificati...
متن کاملUNIBA: Super-sense Tagging at EVALITA 2011
This paper describes our participation in EVALITA 2011 Super Sense Tagging (SST) task. The goal of the task is to annotate each word in a text within a general semantic taxonomy defined by the WordNet lexicographer classes called super-senses. In this task, we exploit structured learning based on Support Vector Machine. Moreover, we propose to solve the data sparseness problem by incorporating ...
متن کاملComputational rule-based model for Irony Detection in Italian Tweets
English. In the domain of Natural Language Processing (NLP), the interest in figurative language is enhanced, especially in the last few years, thanks to the amount of linguistic data provided by web and social networks. Figurative language provides a non-literary sense to the words, thus the utterances require several interpretations disclosing the play of signification. In order to individuat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016