Sentiment analysis of blogs by combining lexical knowledge with text classification

TLDR

The rise of user‑generated content has created opportunities and challenges for companies to monitor product discussions, with sentiment analysis aiming to automatically detect positive or negative opinions in blogs. This paper proposes a unified framework that combines background lexical knowledge with domain‑specific training data to analyze sentiment. The framework refines word‑class associations using available training examples to improve classification. Empirical results across diverse domains show that this approach outperforms using background knowledge or training data alone, as well as other lexical‑classification hybrids.

Abstract

The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or market them more effectively. An important component of such analysis is to characterize the sentiment expressed in blogs about specific brands and products. Sentiment Analysis focuses on this task of automatically identifying whether a piece of text expresses a positive or negative opinion about the subject matter. Most previous work in this area uses prior lexical knowledge in terms of the sentiment-polarity of words. In contrast, some recent approaches treat the task as a text classification problem, where they learn to classify sentiment based only on labeled training data. In this paper, we present a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples. Empirical results on diverse domains show that our approach performs better than using background knowledge or training data in isolation, as well as alternative approaches to using lexical knowledge with text classification.