Exploiting reviewers’ comment histories for sentiment analysis

Abstract

Sentiment analysis is used to extract people’s opinion from their online comments in order to help automated systems provide more precise recommendations. Existing sentiment analysis methods often assume that the comments of any single reviewer are independent of each other and so they do not take advantage of significant information that may be extracted from reviewers’ comment histories. Using psychological findings and the theory of negativity bias, we propose a method for exploiting reviewers’ comment histories to improve sentiment analysis. Furthermore, to use more fine-grained information about the content of a review, our method predicts the overall ratings by aggregating sentence-level scores. In the proposed system, the Dempster–Shafer theory of evidence is utilized for score aggregation. The results from four large and diverse social Web datasets establish the superiority of our approach in comparison with the state-of-the-art machine learning techniques. In addition, the results show that the suggested method is robust to the size of training dataset.

An Evidential Model for Environmental Risk Assessment in Projects Using Dempster–Shafer Theory of Evidence

“…The Dempster-Shafer theory is a theory of uncertainty that is presented to determine the degree of support for an information source from a proposition [19]. In fact, this theory is a substitute for the classical probability theory that lets combining and neglecting evidence, too Basiri et al [23]. This theory was originally presented by Dempster [29], and then Shafer completed it in his book "A mathematical theory of evidence" [30].…”

“…The first step in using Dempster's theory in each problem is to define the propositions [23,24]. In the proposed model, each proposition indicates the amount of belief in the evidence for the relevant risk factor, which is a real number in the range [1,5] as:…”

“…In Equation (8), i has only two possible values because we intend to aggregate just two evidences. The second step in using Dempster's theory is to define the evidence [23]. In the current study, following the natural way in which experts make a decision, we consider the impact and probability as evidence for the final risk value.…”

“…For example, Basir and Yuan [21] utilized the DS theory for engine fault diagnosis and Wahab et al [22] proposed a DS-based model for detecting misbehaving vehicles. Basiri et al [23] exploited the DS theory for predicting the sentiment of users from their comments and Nemati and Naghsh-Nilchi [24,25] used DS theory in multimodal affective video retrieval. More recently, Basiri and Kabiri [26] used the DS theory for aggregating sentiment labels and combining supervised and unsupervised machine learning classifier results for sentiment analysis [27].…”

Analysis of Spatial Interaction between Different Food Cultures in South and North of China — Practices from People's Daily Life

“…The perception and analysis of cultural image based on data such as social media data complies with the research mode of new cultural geography, which emphasizes the bottom-up cultural meaning and value system stems from people's daily life and practice. This paper adopts the geography-related topic Q&A data in Zhihu, the largest domestic Q&A platform, as main data sources, and tries to construct cognition of cultural differences between South and North China through text analysis, POS tagging, proper nouns extraction, and topic words clustering with NLP as core, and carry out emotion analysis under the influence of cultural differences between South and North China [36][37][38].…”