Medical data Opinion retrieval on Twitter streaming data

Sindhura, Vemuri; Yelisetti, Sandeep

doi:10.1109/icecct.2015.7226043

Cited by 4 publications

(3 citation statements)

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“…They expect that equivalent words (antonyms) of a word have the same (inverse) extremity. The level of a word"s equivalent words having a place with arrangements of either extremity was utilized as a proportion of its extremity quality, while those underneaths an edge was esteemed nonpartisan or equivocal [9][10] [11].…”

Section: Existing Systemmentioning

confidence: 99%

“…1) Naïve Bayes Algorithm: In classification of data a machine learning technique, Naive Bayes technique [11] is one of the best methods. For classification of data a "Multinomial Naive Bayes Technique" is used where "d" denotes a news data, "c*" denotes a class which is assigned to data "d", where…”

Section: A Machine Learning Algorithmsmentioning

confidence: 99%

“…2) Maximum Entropy: Maximum Entropy for a text classification is implemented by word counts as features [9] [11]. 1) Support Vector Machine: SVM considered as one of the most significant classification techniques where a set of vectors is taken as an input data with size "m".…”

Section: A Machine Learning Algorithmsmentioning

confidence: 99%

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Performance based Machine Learning Algorithm for Topic Oriented Text Categorization

2019

IJRTE

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With the growth of societal news on the web, public opinions are given major importance in decision-making. Researchers of text-based mining have made number of evaluations and were diversified using different data mining methods so as to make the conclusions positive, negative and neutral. So, opinions of people are considered to mine the social information as people give superfluous interest to the reports. In this paper the newspaper data set is considered to find the opinion mining to evaluate the sentiment. Sentiment Analysis is used to compute the opinions of people before they judge on a particular issue. Machine Learning is one of the important approaches for analysis of sentiments. Different methods like Naïve Bayes, SVM, Maximum entropy and SLDA are used for classifying the sentiments. Predictions based on precision, f-measure, recall are done to determine which method best suits the classification.

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Section: Existing Systemmentioning

confidence: 99%

Section: A Machine Learning Algorithmsmentioning

confidence: 99%