A Survey on Opinion Mining: From Stance to Product Aspect

Wang, Rui; Zhou, Deyu; Jiang, Mingmin; Si, Jiasheng; Yang, Yang

doi:10.1109/access.2019.2906754

Cited by 63 publications

(39 citation statements)

References 112 publications

(207 reference statements)

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“…Wang et al [19] (2019) surveyed on various techniques for stance detection. They categorized their study into 2 main domains; one with textual content only and other where textual content was supplemented with metadata.…”

Section: Sentiment Analysis and Prediction Of Chosen Politicalmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

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Twitter is one of the most popular social networking platforms of today’s generation and is a fundamental tool in harvesting the data of many users worldwide. Many discussions ranging from current affairs, news sharing, filing complaints to advertising and discussing some common interests etc. happen on Twitter. It is widely used by many famous politicians as a prominent communication medium to address large masses ,owing to its mass usage and popularity . Thus, it is safe to assume that people communicate their political ideologies on Twitter. Many works have been done so far to deduce the stance of user towards a particular party by performing sentiment analysis on their tweets using popular classifiers. To find connected and similar users, earlier works generated a social network graph, based on the assumption that friends and followers share similar interests, which might not be true in all cases. In contrast, the proposed work employs the concept of ensemble classifier (a single classifier generated from several base learning classifiers) to analyze the tweets and makes use of multiple interaction elements like followers/ following, mentions, re-tweets ,hash tags etc. to infer which political party a user identifies with. These interaction elements project out the homophily (users who share same beliefs and choices) amongst the users. The proposed study can be encompassed to any domain and can be used by advertising agencies, marketing companies, e-commerce, heath care etc. to identify their target audience.

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Section: Sentiment Analysis and Prediction Of Chosen Politicalmentioning

confidence: 99%

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Shetty

Muniyal

Teja

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the positive words like 'Good' and 'Better' have low to moderate frequency. These (12) findings indicate that the customers are mostly complaining about different aspects of the Uber service. In order to improve the service, Uber can incorporate this aspect based sentiment analysis in their Kansei engineering strategy to uplift their business in the region.…”

Section: B Frequency Analysis Of Clustersmentioning

confidence: 87%

“…Another important source of getting customer feedback is customer reviews available on online social networks. Customers post their reviews on social networks without any biases [12]. Thus, these reviews depict customer's sentiment in the true sense at large scale.…”

Section: Introductionmentioning

confidence: 99%

Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

2020

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At present online reviews are becoming an important source for Kansei engineering of the services provided by ridesharing platforms. Kansei engineering deals with incorporating customer feedback and demands into product and service design. Thus, it is used as a tool for organizations to uplift their businesses by considering customer reviews and feedback. Customer reviews available on social media are in unstructured form; therefore, sentiment analysis is employed to extract customer's opinions in a systematic manner. In India-Pakistan, these reviews are mostly in Roman Urdu/Hindi and English, which are of great value for ridesharing platforms as a part of their Kansei engineering strategy. However, sentiment analysis cannot be performed directly on these reviews as they are mostly in Roman Urdu/Hindi. Therefore, the objective of this paper is to conduct aspect based sentiment analysis on these reviews after translating them into English for Kansei engineering of the service. Consequently, sentiment analysis is carried out to extract the most frequent features along with nouns and adjectives used by the customers to express their sentiments. We extracted prominent aspects of the service (i.e., 'Driver', 'Company', 'Service', and 'Ride') based on their highest frequencies using aspect based sentiment analysis. The customer sentiments are then clustered into these main aspects using unsupervised machine learning technique. Each aspect is further analyzed based on their polarity, which serves as an input for Kansei engineering of the service. As a result, it can facilitate ridesharing companies to enhance their businesses by improving services in accordance with customer demands.

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“…Making use of this data is facilitated through sentiment and emotion analysis which identifies and measures opinions and feelings from text. Achieving this on social media faces numerous challenges such as the use of informal language, abbreviations, lack of training data, the ambiguity of language, the presence of sarcasm and so on [5,22,30,32].…”

Section: Related Workmentioning

confidence: 99%

“…Social media provides citizens with a public platform to discuss many topics and voice their opinions about them. Mining these discussions can provide insights about a variety of topics of interest to government agencies, business enterprises and nonprofit organisations [19,30]. These insights can then be used for a variety of purposes, such as designing better information campaigns, understanding concerns and issues of relevance to citizens, the development of new services and products, improved service delivery, marketing and brand management [26,32].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Mood of Social Media Messages

Power¹,

Robinson²,

Dennett³

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Social media is a valuable source of information when seeking to understand community opinion and sentiment about issues of public interest. Such analysis is usually based on sentiment or emotion processing using machine learning techniques or references a curated lexicon of words to measure the emotive intensity being expressed. The lexicon approach can be limited by the sparsity problem, where the lexicon words are not present in the text being processed, and context issues, where the lexicon words have different meanings in the domain under investigation. We have developed a novel technique based on word embeddings to mitigate these issues and present a case study showing its application, where the mood expressed by the community on social media about the Centenary of Armistice in Australia was determined in near real-time.

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A Survey on Opinion Mining: From Stance to Product Aspect

Cited by 63 publications

References 112 publications

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

WITHDRAWN: Detection of Political Intent Through Analysis of Tweets and Homophily Elements

Aspect Based Sentiment Analysis of Ridesharing Platform Reviews for Kansei Engineering

Understanding the Mood of Social Media Messages

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