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 402 publications

(191 citation statements)

References 23 publications

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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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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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“…Initial studies often presented optimistic results regarding the predictive capacity of Twitter data relative to election results Authors presented optimistic results regarding the predictive capacity of social media towards the election results in different countries [7].The most important factor for better prediction depends on the data or data collection methodology [8]. If the data collected is not much relevant towards the event then the results may be inappropriate [9].…”

Section: Introductionmentioning

confidence: 99%

Towards Prediction of Election Outcomes Using Social Media

Jain¹,

Kumar²

2017

IJISA

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Abstract-Exploiting social media data by extracting key information from it is one of the great challenges in data mining and knowledge discovery. Every election campaign has an online presence of voters which uses these social media platform to express their sentiments and opinions towards political parties, leaders and important topics. This paper presents a novel data collection technique for prediction of election outcomes and a topic modeling method for extracting topics. Data collection technique used RSS (Rich Site Summary) feeds of news articles and trending keywords from Twitter simultaneously and constructed an intelligent prediction model based primarily on the volume of tweets and sentiment of users. This paper effort to improve electoral predictions using social media data based dynamic keyword methodology.Different techniques for electoral prediction based on social media data has been investigated based on existing literature and isolate the factors which improve our methodology. Meaningful inferences such as the popularity of leaders and parties during different intervals, trending issues, and important factors are extracted from the data set. The election outcomes are compared with traditional methods used by survey agencies for exit polls and validation of results showed that social media data can predict with better accuracy. The research has identified that data collection technique and timing play an important role in yielding better accuracy in predicting outcomes and extracting meaningful inferences.

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

Cited by 402 publications

References 23 publications

Classifying Learners’ Cognitive Engagement From Online Discussion Using Text Mining

Classifying Learners’ Cognitive Engagement From Online Discussion Using Text Mining

Exploring the Field of Text Mining

Towards Prediction of Election Outcomes Using Social Media

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