Аннотации:
This paper describes a supervised approach for solving a task on sentiment analysis of tweets about banks and telecom operators. The task was articulated as a separate track in the Sentiment Evaluation for Russian (SentiRuEval-2015) initiative. The approach we proposed and evaluated is based on a Support Vector Machine model that classifies sentiment polarities of tweets. The set of features includes term frequency features, twitter-specific features and lexicon-based features. Given a domain, two types of sentiment lexicons were generated for feature extraction: (i) manually created lexicons, constructed from Pros and Cons reviews; (ii) automatically generated lexicons, based on pointwise mutual information between unigrams in a training set. In the paper we provide results of our method and compare them to results of other teams participated in the track. We achieved 35.2% of macro-averaged F-measure for banks and 44.77% for tweets about telecom operators. The method described in the paper is ranked second and fourth among 7 and 9 teams, respectively. The best SVM setting after tuning parameters of the classifier and error analysis with common types of errors are also presented in this paper.
