Opinion target extraction with sentiment analysis

TLDR: This paper proposes a novel method of extracting aspects using ontology and further categorizing these sentiments into positive, negative and neutral category using supervised leaning technique and efficiency is evaluated using information retrieval search strategies.

Abstract: Social networks have increased their demand extensively for mining texts. Opinions are used to express views and reviews are used to provide information about how a product is perceived. The reviews available online can be available in thousands, so making the right decision to select a product becomes a very tedious task. Several research works has been proposed in the past but they were limited to certain issues discussed in this paper. A dynamic system is proposed based on the features using ontology followed with classification. Classifying information from such text is highly challenging. We propose a novel method of extracting aspects using ontology and further categorizing these sentiments into positive, negative and neutral category using supervised leaning technique. Opinion Mining is a natural language processing task that mine information from various text forums and classify them on the basis of their polarity as positive, negative or neutral. In this paper, we demonstrate machine learning algorithms using WEKA tool and efficiency is evaluated using information retrieval search strategies.