Combining Local and Global Resources for Constructing an Error-Minimized Opinion Word Dictionary

An Error Minimization Algorithm for reducing errors caused by automatic compiling process to construct a reasonable opinion word dictionary is proposed and empirical results show that this approach is effective for enhancing the performance of the sentiment classification task.

Abstract

A lexical dictionary consisting of opinion words and their polar orientations plays a crucial contribution to opinion mining tasks (e.g., sentiment classification). Previous works on automatic construction of such dictionary have a problem of generating errors (i.e., incorrect identification of polar orientations of words in dictionary). To address the problem, this paper proposes an Error Minimization Algorithm for reducing errors caused by automatic compiling process to construct a reasonable opinion word dictionary. The proposed algorithm combines global and local resources for extracting and refining the dictionary with minimum errors. Empirical results show that our proposed approach is effective for enhancing the performance of the sentiment classification task.