Experimental Results on Customer Reviews Using Lexicon-Based Word Polarity Identification Method

Abstract

Determining the polarities of words in a given context has been in existence since the inception of computational linguistics, text mining, and sentiment analysis. Due to its fundamental role in determining the overall semantic orientation of natural language expressions, it is considered one of the most challenging issues facing these areas of research. This paper introduces a new implementation of the lexicon-based word polarity identification method on several customer reviews datasets. Herein, we use a variation of a lexicon-based word polarity identification method that operates by computing the semantic relatedness between the context expansion set of the target word and a synonym expansion set comprising the synonyms of all words surrounding the target word within the original text fragment. The polarity of the target word is determined as that for which the semantic relatedness between these two meaningful sets is the highest. Unlike most existing lexicon-based multi-polarity word identification methods, the used method is not based on estimating pairwise relatedness at term-level, but instead, it is based on measuring semantic relatedness at the fragment-level. This enables the exploration and capture of a higher degree of semantic and sentimental information, and is more consistent with people’ understanding through the consideration of the larger context in which the word appears. Its performance can be further improved by incorporating an initial step in which the relative negation scope of words in the given text fragment is managed while determining their sentiment orientation. The implementation results demonstrate that the used variation of the lexicon-based word polarity identification method performs favourably against compared methods, as evaluated on numerous benchmark datasets through stand-alone and end-to-end evaluation models.