Abstract

We describe two methods to perform sentiment analysis both on long and short texts written in Spanish language. We first present an unsupervised method based on dependency parsing which calculates the semantic orientation (SO) of the sentences in order to classify the polarity. We then propose a hybrid approach which uses the computed SO and lexico-syntactic knowledge as features for a supervised classifier. Experimental results show the utility of employing syntactic information to classify the polarity in both types of texts and the importance of defining mechanisms to adapt the system for a specific domain and social medium.

Highlights

  • With the apparition of Web 2.0 and the rise of blogs, forums and social networks, users express their views about various topics on these sites

  • Semantic-based approaches (Turney (2002)) involve the use of dictionaries where different kinds of words are tagged with their semantic orientation (SO); they have been applied successfully in many contexts but their performance is not optimum because different application domains and social media have many specific subjective elements, this results in a low recall of the opinion lexicons (Zhang et al (2011))

  • We describe an unsupervised and a hybrid method based on linguistic knowledge

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Summary

INTRODUCTION

With the apparition of Web 2.0 and the rise of blogs, forums and social networks, users express their views about various topics on these sites. The recent success of microblogging social networks, such as Twitter, has increased interest in monitoring short texts In this line, Bakliwal et al (2012) performed an sentiment scoring algorithm which uses prior information to classify the polarity of tweets, and Sidorov et al (2012) explore different settings of parameters for a supervised classifier. In this context, this paper proposes an unsupervised and a supervised approach which are able to perform binary polarity classification over reviews and short texts. In order to homogenise experimental results with HOpinion, we only take into account two polarities: positive (P+, P) and negative (N+, N), discarding the rest of the tweets

NATURAL LANGUAGE PROCESSING TASKS
Treatment of intensification
Treatment of subordinate adversative clauses
Treatment of negation
Subjective parent rule
Adjunct rule
Default rule
A SUPERVISED SYSTEM BASED ON LINGUISTIC FEATURES
CONCLUSIONS AND FUTURE WORK

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