Analysis of opinionated text for opinion mining

TL;DR

This paper explores how meta-information and diverse features influence sentiment polarity detection in opinionated texts, aiming to improve sentiment analysis systems.

Contribution

It investigates the role of meta-data and additional features in enhancing sentiment polarity classification beyond traditional word-based methods.

Findings

Meta-information significantly impacts sentiment polarity detection.

Additional features can improve text categorization and spam detection.

Scope for further research in feature utilization for opinion mining.

Abstract

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.