Aspect context aware sentiment classification of online consumer reviews

Abstract

Purpose Aspect based sentiment classification is valuable for providing deeper insight into online consumer reviews (OCR). However, the majority of the previous studies explicitly determine the orientation of aspect related sentiment bearing word and overlook the aspect-context. Therefore, this paper aims to propose an aspect-context aware sentiment classification of OCR for deeper and more accurate insights. Design/methodology/approach In the proposed methodology, first, aspect descriptions and sentiment bearing words are extracted. Then, the skip-gram model is used to extract the first set of features to capture contextual information. For the second category of features, cosine similarity is used between a pre-defined seed word list and aspects, to capture aspect context sensitive sentiments. The third set of features includes weighted word vectors using term frequency-inverse document frequency. After concatenating features, ensemble classifier is used using three base classifiers. Findings Experimental results on two real-world data sets with variable lengths, acquired from Amazon.com and TripAdvisor.com, show that the advised ensemble approach significantly outperforms sentiment classification accuracy of state-of-the-art and baseline methods. Originality/value This method is capable of capturing the correct sentiment of ambiguous words and other special words by extracting aspect-context using word vector similarity instead of expensive lexical resources, and hence, shows superior performance in terms of accuracy as compared to other methods.

Ensemble correction model for aspect-level sentiment classification

“…Non-deep learning models can be used as sub-classifiers. To predict sentiment, Bansal and Srivastava [41] proposed an ensemble model which was composed of three sub-models based on skip-gram, cosine similarity and term frequency–inverse document frequency, respectively. Compared with traditional machine learning models, deep learning models perform better in sentiment analysis [42].…”

Context-Based Emotion Predictor: A Decision- Making Framework for Mobile Data

“…e emotions show the state of people's expression, their decisions, and actions [31,32]. Same polarity tweets have different emotions as in US presidential election 2016 Trump has better favored Hillary Clinton in the early hours.…”

“…Context is important at the interpersonal level in the process of emotional regulation. Based on expectations of the workplace and colleagues when examining the process of emotional regulation, it is clear to focus on the context of emotion regulation [31,36]. Gaining knowledge in this context is still a challenge.…”