Efficient Topic Level Opinion Mining and Sentiment Analysis Algorithm using Latent Dirichlet Allocation Model

B, Vamshi Krishna; Jntua, Anantapuramu

doi:10.30534/ijatcse/2019/105852019

Cited by 1 publication

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“…Sentiment analysis, or opinion mining, refers to the broad area of natural language processing, text mining, computational linguistics, which involves the computational study of sentiments, opinions, and emotions based on emotion expressed in a text [14]. View or attitude based on feeling instead of the reason is often colloquially referred to as a sentiment.…”

Section: Sentiment Analysismentioning

confidence: 99%

Visualizing Communication of Service Providers Reputation During Covid-19 Pandemic: A Conceptual Model

2020

IJATCSE

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This paper presents to fill the gap and proposes a new conceptual model in developing an application to visualizing the reputation of communication service providers (CSP) during the Covid-19 pandemic. The outbreak of the COVID-19 caused a significant increase in the usage of voice and data using CSP. Regardless of it is seems under a protective umbrella during the pandemic, the increasing demand for CSP in a pandemic may cause customers to switch for better service. CSP companies have an abundance of data about their customers; however, the social element mainly the pithy, real-time commentary express via networks such as Twitter is often overlooked. It is due to the widely used NPS (Net Promoter Score) to measure their customers' loyalty and satisfaction. Even some of the telecommunication has started venturing into social media data analytics, the improvements required in detecting the combination of many languages used in blogs and forums. This gap inclusive the short words, not enough sentiment analytics for non-English languages, and obviously, social media in non-English languages favoured comparing to English languages. Therefore, we proposed a comprehensive conceptual model that adapted from two existing conceptual models, Simulation in Modeling CM (2008) and Integrated Framework for CM (2016). We believed it could be a guideline in visualizing the reputation of CSP that involves extracting public tweets from twitter sentiment analysis. As a result, CSP companies can get a more unobstructed view of their reputation, insights about the products and services that their customers appreciate.

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Section: Sentiment Analysismentioning

confidence: 99%

Visualizing Communication of Service Providers Reputation During Covid-19 Pandemic: A Conceptual Model

2020

IJATCSE

View full text Add to dashboard Cite

show abstract

Efficient Topic Level Opinion Mining and Sentiment Analysis Algorithm using Latent Dirichlet Allocation Model

Cited by 1 publication

References 2 publications

Visualizing Communication of Service Providers Reputation During Covid-19 Pandemic: A Conceptual Model

Visualizing Communication of Service Providers Reputation During Covid-19 Pandemic: A Conceptual Model

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