  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.
