A New Approach for A Domain-Independent Turkish Sentiment Seed Lexicon Compilation

Sentiment analysis deals with opinions in documents and relies on sentiment lexicons; however, Turkish is one of the poorest languages in regard to having such ready-to-use sentiment lexicons. In this article, we propose a domain-independent Turkish sentiment seed lexicon, which is extended from an initial seed lexicon, consisting of 62 positive/negative seeds. The lexicon is completed by using the beam search method to propagate the sentiment values of initial seeds by exploiting synonym and antonym relations in the Turkish Semantic Relations Dataset. Consequently, the proposed method assigned 94 words as positive sentiments and 95 words as negative sentiments. To test the correctness of the sentiment seeds and their values the first sense, the total sum and weighted sum algorithms, which are based on SentiWordNet and SenticNet 3, are used. According to the weighted sum, experimental results indicate that the beam search algorithm is a good alternative to automatic construction of a domain-independent sentiment seed lexicon.