  Sentiments of words differ from one corpus to another. Inducing general
sentiment lexicons for languages and using them cannot, in general, produce
meaningful results for different domains. In this paper, we combine contextual
and supervised information with the general semantic representations of words
occurring in the dictionary. Contexts of words help us capture the
domain-specific information and supervised scores of words are indicative of
the polarities of those words. When we combine supervised features of words
with the features extracted from their dictionary definitions, we observe an
increase in the success rates. We try out the combinations of contextual,
supervised, and dictionary-based approaches, and generate original vectors. We
also combine the word2vec approach with hand-crafted features. We induce
domain-specific sentimental vectors for two corpora, which are the movie domain
and the Twitter datasets in Turkish. When we thereafter generate document
vectors and employ the support vector machines method utilising those vectors,
our approaches perform better than the baseline studies for Turkish with a
significant margin. We evaluated our models on two English corpora as well and
these also outperformed the word2vec approach. It shows that our approaches are
cross-domain and portable to other languages.
