Live Twitter Sentiment Analysis

Sharma, Vedanshu; Sharma, Vipul; Shukla, Dhanesh; Tanwar, Poonam; Kumar, Brijesh

doi:10.2139/ssrn.3609792

Cited by 6 publications

(6 citation statements)

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“…Nowadays, researchers are using posts on social media for analysis in order to achieve or predict results. Social media is a great platform to express sentiments, views, and opinions [11]. Twitter is one of the most widely used social media platforms in the world, where users can post anything in their mind.…”

Section: Related Literaturementioning

confidence: 99%

“…Natural language processing (NLP) is used to retrieve information from a given text [11]. This is a process where the computer extracts meaning from sentences made by a human.…”

Section: Related Literaturementioning

confidence: 99%

“…), exclamation point (! ), link tags (http), and mention tags (@), and replaces them with the corresponding text values to facilitate the extraction of words [11].…”

Section: Replacing Tagsmentioning

confidence: 99%

“…By doing so, the process can extract the root word in a term to obtain higher accuracy. For example, the words high, higher, and highest can all be stemmed to the word "high" [11]. 5.…”

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confidence: 99%

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Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

et al. 2021

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A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

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Section: Related Literaturementioning

confidence: 99%

“…Natural language processing (NLP) is used to retrieve information from a given text [11]. This is a process where the computer extracts meaning from sentences made by a human.…”

Section: Related Literaturementioning

confidence: 99%

“…), exclamation point (! ), link tags (http), and mention tags (@), and replaces them with the corresponding text values to facilitate the extraction of words [11].…”

Section: Replacing Tagsmentioning

confidence: 99%

“…By doing so, the process can extract the root word in a term to obtain higher accuracy. For example, the words high, higher, and highest can all be stemmed to the word "high" [11]. 5.…”

mentioning

confidence: 99%

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Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

et al. 2021

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“…Step 6: The trained naïve byes classifier (NBC) and support vector machines (SVM) classifiers gives us the accuracy of the tested data [24][25].…”

Section: Proposed System/archtecturementioning

confidence: 99%

A proposed system for opinion mining using machine learning, NLP and classifiers

Tanwar¹,

rai²

2020

IJ-AI

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In today’s life consumer reviews are the part of everyday life. User read the reviews before purchase, or stores it for finding the best product through comparison of the product review. From customers view point the reviews play vital role to make a decision regarding an online purchase as well as spammers to write the fake reviews which can increase or defame the reputation of any product. Spammers are using these platforms illegally for financial benefits/incentives are involved in writing fake reviews and they are trying to achieve their motive in terms of financial or to defeat the competitor which causes an explosive growth of sentiment/opinion spamming of writing forged/fake reviews. The present studies and research are used to analyse and categorize the opinion spamming into three different detection targets opinion spam, spammers, and to find the collusive opinion spammer groups so that false opinions can be avoided. Opinion spamming further divided into three different types based on textual and linguistic, behavioral, and relational features. The motivation behind this work is to study the dynamics of spam diffusion and extract the latent features that fuel the diffusion process. The user-based features and content-based features have been used for the categorization of spam/non-spam content. The contributions of this work are building the datasetwhich assists as the ground-truth for classifying/analyzing the variation of fraud/genuine and non-spam/spam information diffusion and to analyze the effects of topics over the diffusibility of non-spam and spam evidences/information. The paper, carried out an in-depth analysis of Twitter Spam diffusion.

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Towards Ensemble-Based Imbalanced Text Classification Using Metric Learning

Komamizu

2023

Lecture Notes in Computer Science

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Live Twitter Sentiment Analysis

Cited by 6 publications

References 0 publications

Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

A proposed system for opinion mining using machine learning, NLP and classifiers

Towards Ensemble-Based Imbalanced Text Classification Using Metric Learning

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