Linguistically Aware Semantic Network for Automated Information Tracking

The paper presents the result of an ongoing research into extending the WiSENet semantic network by adding an extra tier of data, which could be applied in a number of text processing tasks. It was envisaged that this new tier of data could provide a more detailed description of adjectives and adverbs contained in the semantic network. This detailed description was centrally concerned with the construction of sentiment vectors that convey data on whether a given term of interest is positive, negative or neutral. To this end, an experiment was carried out which made it possible to build such sentiment vectors. In the building phase a corpus of over 12000 documents containing opinions on hotels, books and movies along with corresponding satisfaction scores was used.. In order to check if sentiment vectors are a useful instrument for performing text processing tasks, a test case was proposed to help prepare the satisfaction level prognosis for the tested documents. In the course of the research it became apparent that not all sentiment vectors were eligible for use in the prognosis. In order to uncover plausible reasons for the ineligibility a linguistic analysis was carried out. Finally, an experiment designed to evaluate the usefulness of sentiment vectors and the level of accuracy of the prognosis based on them was performed. It had a success rate of 61.33%, which is highly satisfactory in comparison to other studies.