ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 644–647, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics ej-sa-2017 at SemEval-2017 Task 4: Experiments for Target oriented Sentiment Analysis in Twitter Enkhzol Dovdon and Jos´e Saias DI - ECT - Universidade de ´Evora Rua Rom˜ao Ramalho, 59 7000-671 ´Evora, Portugal d36506@alunos.uevora.pt, jsaias@uevora.pt Abstract This paper describes the system we have used for participating in Subtasks A (Mes- sage Polarity Classification) and B (Topic- Based Message Polarity Classification ac- cording to a two-point scale) of SemEval- 2017 Task 4 Sentiment Analysis in Twit- ter. We used several features with a sen- timent lexicon and NLP techniques, Max- imum Entropy as a classifier for our sys- tem. 1 Introduction Text data has been growing dramatically. We have demands to process and mine from Social net- works and online platforms. Opinions in user- generated content, are valuable for market and trend analysis. Processing of sentiment analysis helps us to automatically distinguish from these written opinions. This paper describes a participation in SemEval-2017 Task 4 with the ej-sa-2017 system. We have participated in SemEval-2017 Task 4 on Sentiment Analysis in Twitter, subtasks A (Message Polarity Classification), B (Topic- Based Message Polarity Classification)(Rosenthal et al., 2017). Subtask A is to classify message polarity from given a message that is of positive, negative, or neutral sentiment. Subtask B is to classify positive or negative sentiment of a tweet towards that topic on a two-point scale. We utilized a supervised machine learning clas- sifier, having bag-of-word (BoW), lemmas, bi- grams of adjective, punctuation based features, and lexicon-based features. The rest of the paper is structured as follows: In Section 2, we present some related work in features and approaches with a lexicon. In Section 3, this section describes the algorithm and feature representation used to detect sentiment of text. In Section 4, the experimental results are introduced. Finally, the conclusions as well as further work are described in Section 5. 2 Related Work There are many works associated with the target- oriented sentiment analysis. Some of these works have focused on probability distribution model of particular features and approach. The system of Sentiue (Saias, 2015) used a separate MaxEnt clas- sifier of MALLET (MAchine Learning for Lan- guagE Toolkit) (McCallum, 2002) with bag-of- word like features (lemmas, bigram, presences, etc.) for Aspect based Sentiment Analysis in SemEval-2015 Task 12 and accuracy was approx- imately 79%. Kamps (Kamps et al., 2004) de- veloped a simple distance measure, that focuses almost exclusively on taxonomic relations and WordNet and determined usage of the semantic orientation of adjectives. Pak (Pak and Paroubek, 2010) utilized the presence of n-grams, for n∈ {1, 2, 3}, as a binary feature of a BoW represen- tation using TreeTagger. They collected a corpus of 300000 text posts from Twitter. Fong (Fong et al., 2013) focused on news articles, which tend to use a more neutral vocabulary using MALLET to implement and train six classifiers for senti- ment analysis and compared …

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