Using Linked Data for polarity classification of patients’ experiences

Abstract

Polarity classification is the main subtask of sentiment analysis and opinion mining, well-known problems in natural language processing that have attracted increasing attention in recent years. Existing approaches mainly rely on the subjective part of text in which sentiment is expressed explicitly through specific words, called sentiment words. These approaches, however, are still far from being good in the polarity classification of patients' experiences since they are often expressed without any explicit expression of sentiment, but an undesirable or desirable effect of the experience implicitly indicates a positive or negative sentiment. This paper presents a method for polarity classification of patients' experiences of drugs using domain knowledge. We first build a knowledge base of polar facts about drugs, called FactNet, using extracted patterns from Linked Data sources and relation extraction techniques. Then, we extract generalized semantic patterns of polar facts and organize them into a hierarchy in order to overcome the missing knowledge issue. Finally, we apply the extracted knowledge, i.e., polar fact instances and generalized patterns, for the polarity classification task. Different from previous approaches for personal experience classification, the proposed method explores the potential benefits of polar facts in domain knowledge aiming to improve the polarity classification performance, especially in the case of indirect implicit experiences, i.e., experiences which express the effect of one entity on other ones without any sentiment words. Using our approach, we have extracted 9703 triplets of polar facts at a precision of 92.26 percent. In addition, experiments on drug reviews demonstrate that our approach can achieve 79.78 percent precision in polarity classification task, and outperforms the state-of-the-art sentiment analysis and opinion mining methods.

SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis

“…In the past, SenticNet has been employed for many different tasks other than polarity detection, e.g., recommendation systems [24], stock market prediction [31], political forecasting [46], irony detection [60], drug effectiveness measurement [42], depression detection [14], mental health triage [1], vaccination behavior detection [27], psychological studies [29], and more. Figure 8: Sentiment data flow for the sentence "The car is very old but rather not expensive" using linguistic patterns.…”

A Semantic Framework based on Domain Knowledge for Opinion Mining of Drug Reviews

“…As shown in Figure 1, the information available on the web, including those expressed on the review sites or domain knowledge, can be used as the input of the resource construction module. In our previous works, we proposed two methods for constructing a knowledge base of polar facts, called FactNet, from domain knowledge (Noferesti & Shamsfard, 2015b), and a knowledge base of indirect opinions, called OpinionKB, from patients' opinions about drugs (Noferesti & Shamsfard, 2015a). In this paper, we combine these two knowledge bases.…”

“…In summary, the main contribution of this paper is to provide a semantic framework based on domain knowledge for indirect opinion mining of drug reviews. In this regard, we integrated our previously proposed methods of constructing static resources (Noferesti & Shamnfard, 2015a;2015b;Noferesti & Shamnfard, 2016) and advanced some new methods to build context-aware resources. To the best of our knowledge, this paper is the first that combines built-in semantic knowledge in the domain-specific resources with textual clues and side information in order to provide a more abstract and richer model of the context.…”

Tehran stock exchange prediction using sentiment analysis of online textual opinions

“…It is noteworthy that, besides the stock market domain, there is a wide range of research on sentiment analysis in other domains, such as box office prediction (Du, Xu, & Huang, 2014;Yu, Liu, Huang, & An, 2012), business analytics (Coussement & Van den Poel, 2009;Kang & Park, 2014), fraud detection (Goel & Uzlem, 2016), recommender systems (Li & Shiu, 2012), the medical domain (Noferesti & Shamsfard, 2015), and so on.…”