Learning representations from heterogeneous network for sentiment classification of product reviews

There have been increasing interests in natural language processing to explore effective methods in learning better representations of text for sentiment classification in product reviews. However, most existing methods do not consider subtle interplays among words appeared in review text, authors of reviews and products the reviews are associated with. In this paper, we make use of a heterogeneous network to model the shared polarity in product reviews and learn representations of users, products they commented on and words they used simultaneously. The basic idea is to first construct a heterogeneous network which links users, products, words appeared in product reviews, as well as the polarities of the words. Based on the constructed network, representations of nodes are learned using a network embedding method, which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the product reviews, including IMDB, Yelp 2013 and Yelp 2014 datasets, show that the proposed approach achieves the state-of-the-art performance

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Effects of Sentiment on Impulsive Buying Behavior: Evidence of COVID-19 in Indonesia

Abstract This study aims to investigate the effects of positive and negative sentiment on impulsive buying behavior among Indonesia people based on the theory of stimulus organism response (S-O-R). First, it examines how COVID-19 information, information credibility, and scarcity affect positive sentiment and negative sentiment. Second, it verifies the influence of positive sentiment and negative sentiment on impulsive buying tendencies and impulsive buying behavior. Third, this study verifies impulsive buying tendency impacts impulsive buying behavior. Data was collected from Indonesian people living in a COVID-19 red zone with an online survey via Google form. In total, 320 respondents completed the survey and data analysis employs confirmatory factor analysis (CFA) and structural equation modelling (SEM).  The result found that COVID-19 information and information credibility have a positive effect on positive sentiment, while it has an insignificant effect on negative sentiment. Scarcity has a positive effect on negative sentiment; on the other hand, it has no significant effect on positive sentiment. Both positive sentiment and negative sentiment have positive effects on impulsive buying tendencies.  Only positive sentiment has a positive effect on impulsive buying behavior, while negative sentiment does not. Finally, impulsive buying tendencies have a positive effect on impulsive buying behavior.   AbstrakPenelitian ini bertujuan untuk menginvestigasi pengaruh positif sentimen dan negative sentimen terhadap perilaku pembelian tidak terencana masyarakat Indonesia berpijak pada teori stimulus organism response (S-O-R). Pertama, penelitian ini menguji bagaimana pengaruh informasi tentang COVID-19, kredibilitas informasi, dan kelangkaan terhadap sentimen positif dan sentimen negatif. Kedua, memverifikasi pengaruh sentimen positif dan sentimen negatif terhadap kecenderungan untuk melakukan pembelian tidak terencana dan perilaku pembelian tidak terencana. Ketiga, memverifikasi pengaruh kecenderungan untuk melakukan pembelian tidak terencana dan perilaku pembelian tidak terencana. Pengumpulan data penelitian ini dilakukan terhadap orang-orang Indonesia yang tingga di zona merah COVID-19 melalui survey online dengan Google form. Secara total ada 320 responden berpartisipasi dalam survey ini, kemudian data dianalisis menggunakan analisis confirmatory (CFA) dan struktural equation modeling (SEM). Hasilnya menunjuukan bahwa informasi tentang COVID-19 dan kredibilitas informasi mempunyai pengaruh positif terhadap sentimen positif, tetapi tidak mempunyai pengaruh yang signifikan terhadap sentimen negatif. Kelangkaan mempunyai pengaruh positif terhadap sentimen negatif, sebaliknya tidak mempunyai pengaruh yang signifikan terhadap sentimen positif. Baik sentimen positif maupun sentimen negatif mempunyai pengaruh positif terhadap kecenderungan untuk melakukan pembelian tidak terencana. Hanya, sentimen positif yang mempunyai pengaruh positif terhadap perilaku pembelian tidak terencana, sedangkan sentimen negatif tidak berpengaruh. Terakhir, kecenderungan untuk melakukan pembelian tanpa rencana mempunya pengaruh positif terhadap perilaku pembelian tidak terencana

Hierarchical viewpoint discovery from tweets using Bayesian modelling

When users express their stances towards a topic in social media, they might elaborate their viewpoints or reasoning. Oftentimes, viewpoints expressed by different users exhibit a hierarchical structure. Therefore, detecting this kind of hierarchical viewpoints offers a better insight to understand the public opinion. In this paper, we propose a novel Bayesian model for hierarchical viewpoint discovery from tweets. Driven by the motivation that a viewpoint expressed in a tweet can be regarded as a path from the root to a leaf of a hierarchical viewpoint tree, the assignment of the relevant viewpoint topics is assumed to follow two nested Chinese restaurant processes. Moreover, opinions in text are often expressed in un-semantically decomposable multi-terms or phrases, such as ‘economic recession’. Hence, a hierarchical Pitman–Yor process is employed as a prior for modelling the generation of phrases with arbitrary length. Experimental results on two Twitter corpora demonstrate the effectiveness of the proposed Bayesian model for hierarchical viewpoint discovery

Learning representations from heterogeneous network for sentiment classification of product reviews

There have been increasing interests in natural language processing to explore effective methods in learning better representations of text for sentiment classification in product reviews. However, most existing methods do not consider subtle interplays among words appeared in review text, authors of reviews and products the reviews are associated with. In this paper, we make use of a heterogeneous network to model the shared polarity in product reviews and learn representations of users, products they commented on and words they used simultaneously. The basic idea is to first construct a heterogeneous network which links users, products, words appeared in product reviews, as well as the polarities of the words. Based on the constructed network, representations of nodes are learned using a network embedding method, which are subsequently incorporated into a convolutional neural network for sentiment analysis. Evaluations on the product reviews, including IMDB, Yelp 2013 and Yelp 2014 datasets, show that the proposed approach achieves the state-of-the-art performance

Text Classification: Exploiting the Social Network

Within the context of social networks, existing methods for document classification tasks typically only capture textual semantics while ignoring the text’s metadata, e.g., the users who exchange emails and the communication networks they form. However, some work has shown that incorporating the social network information in addition to information from language is useful for various NLP applications, including sentiment analysis, inferring user attributes, and predicting interpersonal relations. In this thesis, we present empirical studies of incorporating social network information from the underlying communication graphs for various text classification tasks. We show different graph representations for different problems. Also, we introduce social network features extracted from these graphs. We use and extend graph embedding models for text classification. Our contributions are as follows. First, we have annotated large datasets of emails with fine-grained business and personal labels. Second, we propose graph representations for the social networks induced from documents and users and apply them on different text classification tasks. Third, we propose social network features extracted from these structures for documents and users. Fourth, we exploit different methods for modeling the social network of communication for four tasks: email classification into business and personal, overt display of power detection in emails, hierarchical power detection in emails, and Reddit post classification. Our main findings are: incorporating the social network information using our proposed methods improves the classification performance for all of the four tasks, and we beat the state-of-the-art graph embedding based model on the three tasks on email; additionally, for the fourth task (Reddit post classification), we argue that simple methods with the proper representation for the task can outperform a state-of-the-art generic model