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Author

Lijo V.P

Bio: Lijo V.P is an academic researcher from VIT University. The author has contributed to research in topics: Sentiment analysis & Big data. The author has co-authored 1 publications.

Papers
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Proceedings ArticleDOI
Princy Victor1, Lijo V.P1
15 Mar 2019
TL;DR: Lambda architecture for polarity detection of tweets has been proposed which analyses both streaming and historical data which can be analysed in parallel and used for certain predictive and analytic purposes.
Abstract: Big Data refers to the extremely big datasets that are produced from different areas which exhibits certain trends and associations. Major areas of big data include medical data, sensor data, social networks such as facebook, twitter, youtube etc. Among this, social networks produce large amount of data per millisecond which can be analysed for several predictive and analytic purposes. Tweets produced by twitter is used in sentiment analysis and polarity detection that helps in identifying the attitude, polarity of words, text or documents. Applying polarity detection in big data is a tedious task as it includes both historical and streaming data. Several frameworks have been proposed for analysing both historical and streaming data in big data. In this paper, lambda architecture for polarity detection of tweets has been proposed which analyses both streaming and historical data. Both the data can be analysed in parallel and used for certain predictive and analytic purposes.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: The authors analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools.
Abstract: The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population's mental health and health programs. It is complex for public organizations to measure the population's mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population's mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter's usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.

3 citations

Book ChapterDOI
01 Jan 2022
TL;DR: In this article , the authors build a resilient and flexible system that allows them to track and measure the sentiment changes of a given population, in our case, the Mexican people, in response to the COVID-19 pandemic.
Abstract: Abstract The emergence of the COVID-19 pandemic has led to an unprecedented change in the lifestyle routines of millions of people. Beyond the multiple repercussions of the pandemic, we are also facing significant challenges in the population’s mental health and health programs. Typical techniques to measure the population’s mental health are semiautomatic. Social media allow us to know habits and daily life, making this data a rich silo for understanding emotional and mental well-being. This study aims to build a resilient and flexible system that allows us to track and measure the sentiment changes of a given population, in our case, the Mexican people, in response to the COVID-19 pandemic. We built an extensive data system utilizing modern cloud-based serverless architectures to analyze 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. We provide metrics, advantages, and challenges of developing serverless cloud-based architectures for a natural language processing project of a large magnitude.