HSAS: Hindi Subjectivity Analysis System

With the development of Web 2.0, we are abundant with the documents expressing user´s opinions, attitudes and sentiments in the textual form. This user generated textual content is an important source of information to make sound decisions by the organizations and the government. The textual information can be categorized into two types: facts and opinions. Subjectivity analysis is the automatic extraction of subjective information from the opinions posted by users and divides the content into subjective and objective sentences. Most of the works in subjectivity analysis exists for English language data but with the introduction of unicode standards UTF-8, Hindi language content on the web is growing very rapidly. In this paper, Hindi Subjectivity Analysis System (HSAS) is proposed. It explores two different methods of generating subjectivity lexicon using the available resources in English language and their comparative evaluation in performing the task of subjectivity analysis at the sentence level. The first method uses English language OpinionFinder subjectivity lexicon. The second method uses a small seed word list of Hindi language and expands it to generate subjectivity lexicon. Different evaluation strategies are used to validate the lexicon. We achieved 71.4% agreement with human annotators and ~80% accuracy in classification on a parallel data set in English and Hindi. Extensive simulations conducted on the test dataset confirm the validity of the suggested method.