Using Twitter for Tapping Public Minds, Predict Trends and Generate Value

Kadambari, Sanchita; Jaswal, Kalpana; Kumar, Praveen; Rawat, Seema

doi:10.1109/acct.2015.99

Cited by 12 publications

(3 citation statements)

References 16 publications

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“…This paper as mentioned makes use of CNN for classification and recurrent neural network (RNN) in order to increase the performance. Sanchita Kadambari, Kalpana Jaswal, Praveen Kumar, Seema Rawat [2] has proposed that Twitter has emerged as a popular micro-blogging platform in expressing one's opinion and ideas. It is because on twitter millions of users are tweeting each and every second which eventually generates a continuous influx of real time data.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Opinion Behavior using Content based Sequential Opinion Influence Model

Elangovan¹

2019

IJRASET

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Micro-blogging and social networking sites play a vital role in identifying and extracting distinct opinions and emotions from public. Social media has become a viral medium for public to express and exchange their opinions through the user-generated text. The aim of this paper is to classify the tweets using Content based algorithm and Support Vector Machine (SVM). The tweets are also analyzed and classified in terms of polarity: Positive, Negative and Neutral. To keep track of user opinion behaviors and infer user opinion influence from the historical exchanged textual information, we develop a contentbased sequential opinion influence framework with the help of two opinion sentiment prediction models with alternative prediction strategies are proposed.

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Section: Related Workmentioning

confidence: 99%

Prediction of Opinion Behavior using Content based Sequential Opinion Influence Model

Elangovan¹

2019

IJRASET

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“…There are two types of breast tumors: malignant and benign [17], [18], [19]. As cells in the breast tissue become isolated and without the usual controls on cell passing and cell division, a malignant tumor develops [20]. Benign tumors have a good contour and are not harmful.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Khorshid

Abdulazeez

Sallow

2021

AJRCoS

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Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

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“…SenthilkumarMeyyappan.et.al / Journal on Electronic and Automation Engineering, 2(4) December 2023,[9][10][11][12][13][14][15][16][17][18] …”

mentioning

confidence: 99%

Improving Weather Forecasting Accuracy Using Machine Learning

2024

jeae

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Weather forecasting has several applications in our daily lives, ranging from agriculture to event planning. Previous weather forecasting models relied on a complex combination of mathematical instruments, which was insufficient to achieve a higher categorization rate. We offer fresh revolutionary approaches for estimating monthly rainfall using machine learning algorithms in this study. Weather forecasts are created by gathering quantitative information about the current state of the atmosphere. Machine learning algorithms may learn complicated mappings from inputs to outputs using only samples and with little effort. The dynamic nature of the atmosphere makes accurate weather prediction challenging. The fluctuation in weather conditions in previous years must be used to anticipate future weather conditions. It is extremely likely that it will match within the next two weeks of the preceding year. We proposed using linear regressions with the Random forest algorithm to forecast weather using characteristics such as temperature, humidity and wind. It will forecast weather based on prior records thus, this prediction will be accurate.

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Using Twitter for Tapping Public Minds, Predict Trends and Generate Value

Cited by 12 publications

References 16 publications

Prediction of Opinion Behavior using Content based Sequential Opinion Influence Model

Prediction of Opinion Behavior using Content based Sequential Opinion Influence Model

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Improving Weather Forecasting Accuracy Using Machine Learning

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