Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9

Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pages 546–550, Dublin, Ireland, August 23-24, 2014. Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9 Jos´e Saias DI - ECT - Universidade de ´Evora Rua Rom˜ao Ramalho, 59 7000-671 ´Evora, Portugal jsaias@uevora.pt Abstract This document describes the senti.ue system and how it was used for partici- pation in SemEval-2014 Task 9 challenge. Our system is an evolution of our prior work, also used in last year’s edition of Sentiment Analysis in Twitter. This sys- tem maintains a supervised machine learn- ing approach to classify the tweet overall sentiment, but with a change in the used features and the algorithm. We use a re- stricted set of 47 features in subtask B and 31 features in subtask A. In the constrained mode, and for the five data sources, senti.ue achieved a score between 78,72 and 84,05 in subtask A, and a score between 55,31 and 71,39 in sub- task B. For the unconstrained mode, our score was slightly below, except for one case in subtask A. 1 Introduction This paper describes the approach taken by a team of Universidade de ´Evora’s Computer Sci- ence Department in SemEval-2014 Task 9: Senti- ment Analysis in Twitter (Rosenthal et al., 2014). SemEval-2014 Task 9 has an expression-level (subtask A) and a message-level (subtask B) polar- ity classification challenges. The first subtask aims to determine whether a word (or phrase) is posi- tive, negative or neutral, within the textual context in which it appears. The second subtask concerns the classification of the overall text polarity, which corresponds to automatically detecting the senti- ment expressed in a Twitter message. In both sub- tasks, systems can operate in constrained or un- constrained mode. Constrained means that learn- This work is licenced under a Creative Commons Attribution 4.0 International License. Page numbers and proceedings footer are added by the organizers. License details: http: //creativecommons.org/licenses/by/4.0/ ing is based only on provided training texts, with the possible aid of static resources such as lexi- cons. Extra tweets or additional annotated doc- uments for training are permitted only in uncon- strained mode. The system we used to respond to this challenge is called senti.ue, and follows on from our previous work on Natural Language Processing (NLP) and Sentiment Analysis (SA). We devel- oped work in automatic reputation assessment, us- ing a Machine Learning (ML) based classifier for comments with impact on a particular target entity (Saias, 2013). We also participated in the previ- ous edition of SemEval SA task, where we have implemented the basis for the current system. In last year’s solution (Saias and Fernandes, 2013), we treated both subtasks using the same method (except the training set). We have updated the method for subtask A, now considering also the text around the area to classify, by dedicating new features to those preceding and following tweet parts. Text overall sentiment classification is the core objective of our system, and is performed, as before, with a supervised machine …

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