Sentiment Analysis of Digital Wallets and UPI Systems in India Post Demonetization Using IBM Watson

BACKGROUND Clinical data present in social media is an underused source of information with great potential to allow for a deeper understanding of patient values, attitudes and preferences. OBJECTIVE We describe a novel and broadly applicable method for sentiment analysis and emotion detection to free text from online medical health forums and the factors to consider during its application. METHODS We mined the full discussion and user information of all posts containing search terms related to a specific medical subspecialty (oculoplastics) from MedHelp, the largest online platform for patient health forums. We employed a variety of data cleaning and processing to define the relevant subset of results and prepare those results for sentiment analysis. We executed sentiment and emotion analysis through IBM Watson Natural Language Understanding service to generate sentiment and emotion scores for the posts and their associated keywords. Keywords were aggregated using natural language processing tools. RESULTS 39 oculoplastics-related search terms resulted in 46,381 eligible posts within 14,329 threads, written by 18,319 users (117 doctors; 18,202 patients) and 201,611 associated keywords. Keywords that occurred ≥500 times in the corpus were used to identify most prominent topics, including specific symptoms, medication and complications. The sentiment and emotion scores of these keywords and eligible posts were further analyzed to provide concrete examples of the methodology’s potential to allow better understanding of patients’ attitudes. CONCLUSIONS This comprehensive report allows physicians and researchers to efficiently mine and perform sentiment analysis on social media to better understand patients’ perspectives and promote patient-centric care. Important factors to be considered during application include evaluating the scope of the search, selecting search terms and understanding their different linguistic usages, and establishing robust selection, filtering and processing criteria for posts and keywords tailored to the results.

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Approach for Importance–Performance Analysis of Product Attributes From Online Reviews

Journal of Mechanical Design ◽

10.1115/1.4049865 ◽

2021 ◽

Vol 143 (8) ◽

Author(s):

Junegak Joung ◽

Harrison M. Kim

Keyword(s):

Neural Network ◽

Performance Analysis ◽

Sentiment Analysis ◽

High Performance ◽

Latent Dirichlet Allocation ◽

Online Reviews ◽

Product Attributes ◽

Ibm Watson ◽

Promising Source

Abstract The importance–performance analysis (IPA) is a widely used technique to guide strategic planning for the improvement of customer satisfaction. Compared with surveys, numerous online reviews can be easily collected at a lower cost. Online reviews provide a promising source for the IPA. This paper proposes an approach for conducting the IPA from online reviews for product design. Product attributes from online reviews are first identified by latent Dirichlet allocation. The performance of the identified attributes is subsequently estimated by the aspect-based sentiment analysis of IBM Watson. Finally, the importance of the identified attributes is estimated by evaluating the effect of sentiments of each product attribute on the overall rating using an explainable deep neural network. A Shapley additive explanation-based method is proposed to estimate the importance values of product attributes with a low variance by combining the effect of the input features from multiple optimal neural networks with a high performance. A case study of smartphones is presented to demonstrate the proposed approach. The performance and importance estimates of the proposed approach are compared with those of previous sentiment analysis and neural network-based method, and the results exhibit that the former can perform IPA more reliably. The proposed approach uses minimal manual operation and can support companies to take decisions rapidly and effectively, compared with survey-based methods.

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Study of Sentiment of Governor's Election Opinion in 2018

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841124 ◽

2018 ◽

pp. 231-238

Author(s):

Agung Eddy Suryo Saputro ◽

Khairil Anwar Notodiputro ◽

Indahwati A

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Addition Method ◽

Sentiment Mining ◽

Positive Sentiment ◽

An historical analysis of species references in American English

Corpora ◽

10.3366/cor.2019.0177 ◽

2019 ◽

Vol 14 (3) ◽

pp. 327-349

Author(s):

Craig Frayne

Keyword(s):

Environmental Change ◽

Sentiment Analysis ◽

Quantitative Methods ◽

English Language ◽

Language Use ◽

American English ◽

Historical Analysis ◽

The Past ◽

Corpus Studies ◽

Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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