Using text mining and sentiment analysis for online forums hotspot detection and forecast

Li, Nan; Wu, Desheng Dash

doi:10.1016/j.dss.2009.09.003

Cited by 419 publications

(210 citation statements)

References 23 publications

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“…It is often used in determining the sentiment of a text. It encompasses studying peoples' attitudes, feelings and opinions towards a product, an event or organization computationally (Kasture & Bhilare, 2017;Li & Wu, 2010;Thomas, et al, 2011). It can be used to assess reviews posted by people online about their decisions regarding the food they consume, items the use and other issues affecting them.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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The Sentimental Analysis (SA) is a widely known and used technique in the natural language processing realm. It is often used in determining the sentiment of a text. It can be used to perform social media analytics. This study sought to compare two algorithms; Logistic Regression, and Support Vector Machine (SVM) using Microsoft Azure Machine Learning. This was demonstrated by performing a series of experiments on three Twitter datasets (TD). Accordingly, data was sourced from Twitter a microblogging platform. Data were obtained in the form of individuals' opinions, image, views, and twits from Twitter. Azure cloud-based sentiment analytics models were created based on the two algorithms. This work was extended with more in-depth analysis from another Master research conducted lately. Results confirmed that Microsoft Azure ML platform can be used to build effective SA models that can be used to perform data analytics.

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Section: Introductionmentioning

confidence: 99%

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Harfoushi¹,

Hasan²,

Obiedat³

2018

MAS

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“…Nan Li & all wanted to analyze the emotional dimension by developing an algorithm that automatically identifies the emotional polarity of a text and then combines it with two Text mining techniques, namely k-means clustering and SVM [13]. This approach is based on the concept of a hotspot, which is used as a cluster center and is determined from the hotspot history.…”

Section: Svm (Support Vectormentioning

confidence: 99%

Classifying Learners’ Cognitive Engagement From Online Discussion Using Text Mining

2017

IJLTET

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“…In reference [15][16] they have studied sentiment analysis and opinion mining from text and how it can be used for forecasting purposes. Sentiment analysis from social community forum blogs can reveal customers' opinion or emotion polarity about products and services.…”

Section: Customermentioning

confidence: 99%

Exploring the Field of Text Mining

Guha¹

2017

IJCA

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Text mining is the technique of automatically deducing nonobvious but statistically supported novel information from various text data sources written in natural languages. In the big data and cloud computing era of today huge amount of text data are getting generated online. Thus text mining is becoming very essential for business intelligence extraction as volume of internet data generation is growing exponentially. Next generation computing is going to see text mining amongst other disruptive technologies like semantic web, mobile computing, big data generation, and cloud computing phenomena. Text mining needs proven techniques to be developed for it to be most effective. Even though structured data mining field is very active and mature, unstructured text mining field has just emerged.

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Using text mining and sentiment analysis for online forums hotspot detection and forecast

Cited by 419 publications

References 23 publications

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Sentiment Analysis Algorithms through Azure Machine Learning: Analysis and Comparison

Classifying Learners’ Cognitive Engagement From Online Discussion Using Text Mining

Exploring the Field of Text Mining

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