Analysis of opinionated text for opinion mining
This work addresses the problem of improving sentiment analysis accuracy for researchers and practitioners by exploring additional features, but it appears incremental as it builds on existing methods without introducing a new paradigm.
The paper investigates the role of meta-information and diverse features in sentiment analysis, finding that these elements significantly influence sentiment polarity and system performance, with potential applications in text categorization, ranking, spam identification, and polarity classification.
In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let us say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.