Pipelining Semantic Expansion and Noise Filtering for Sentiment Analysis of Short Documents – CluSent Method

Abstract

The challenge of constructing effective sentiment models is exacerbated by a lack of sufficient information, particularly in short texts. Enhancing short texts with semantic relationships becomes crucial for capturing affective nuances and improving model efficacy, albeit with the potential drawback of introducing noise. This article introduces a novel approach, CluSent, designed for customized dataset-oriented sentiment analysis. CluSent capitalizes on the CluWords concept, a proposed powerful representation of semantically related words. To address the issues of information scarcity and noise, CluSent addresses these challenges: (i) leveraging the semantic neighborhood of pre-trained word embedding representations to enrich document representation and (ii) introducing dataset-specific filtering and weighting mechanisms to manage noise. These mechanisms utilize part-of-speech and polarity/intensity information from lexicons. In an extensive experimental evaluation spanning 19 datasets and five state-of-the-art baselines, including modern transformer architectures, CluSent emerged as the superior method in the majority of scenarios (28 out of 38 possibilities), demonstrating noteworthy performance gains of up to 14% over the strongest baselines.