Recognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis

Abstract

Many approaches to automatic sentiment analysis begin with a large lexicon of words marked with their prior polarity (also called semantic orientation). However, the contextual polarity of the phrase in which a particular instance of a word appears may be quite different from the word's prior polarity. Positive words are used in phrases expressing negative sentiments, or vice versa. Also, quite often words that are positive or negative out of context are neutral in context, meaning they are not even being used to express a sentiment. The goal of this work is to automatically distinguish between prior and contextual polarity, with a focus on understanding which features are important for this task. Because an important aspect of the problem is identifying when polar terms are being used in neutral contexts, features for distinguishing between neutral and polar instances are evaluated, as well as features for distinguishing between positive and negative contextual polarity. The evaluation includes assessing the performance of features across multiple machine learning algorithms. For all learning algorithms except one, the combination of all features together gives the best performance. Another facet of the evaluation considers how the presence of neutral instances affects the performance of features for distinguishing between positive and negative polarity. These experiments show that the presence of neutral instances greatly degrades the performance of these features, and that perhaps the best way to improve performance across all polarity classes is to improve the system's ability to identify when an instance is neutral.

A Hybrid Approach for Polarity Shift Detection

“…A document contains multiple opinions. The task at phrase level is to determine the opinion that is being expressed by a phrase [28]. Aspect level performs fine-grained analysis.…”

Main Concepts, State of the Art and Future Research Questions in Sentiment Analysis

“…However, some Unsupervised Learning techniques have been very successful as well, as it is unsupervised technique based on the PMI-IR algorithm that is used to estimate the semantic orientation of a phrase by measuring the similarity of pairs of words or phrases [60]. Alternative methods have been proposed, like the Bootstrapping Method for Building Subjectivity Lexicons for Languages with Scarce Resources [6], the techniques for generating a quality lexicon [59], the recognition of contextual polarity in [69] and the gradability of subjective sentences based on adjective orientation [26]. In all these cases, the focus of the research is at the sentence/phrase level.…”

Automatic Detection of Political Opinions in Tweets

“…The context in which a term is used can change its meaning, which is particularly true for adjectives in sentiment lexicons [3]. Several evaluations have shown that sentiment detection methods should not neglect contextual information [4,5], and have identified context words with a high impact on the polarity of ambiguous terms [6]. Besides the ambiguity of human language, another bottleneck for sentiment detection methods is the time-consuming creation of sentiment dictionaries.…”