Sentiment Analysis of Tweets using SVM

Ahmad, Munir; Aftab, Shabib; Ali, Iftikhar

doi:10.5120/ijca2017915758

Cited by 111 publications

(69 citation statements)

References 18 publications

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“…The reason of including the output class in dataset among other features is to analyze the performance and accuracy of data mining techniques [20], [24]. The output result after processing is compared with the known class and performance is measured in terms of precision, recall and f measure [1], [20], [21], [24], [26]. Weka [22], [23] is used in this study for classification and performance analysis.…”

Section: Methodsmentioning

confidence: 99%

Rainfall Prediction in Lahore City using Data Mining Techniques

Aftab¹,

Ahmad²,

Hameed³

et al. 2018

ijacsa

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Abstract-Rainfall prediction has extreme significance in countless aspects and scopes. It can be very helpful to reduce the effects of sudden and extreme rainfall by taking effective security measures in advance. Due to climate variations, an accurate rainfall prediction has become more complex than before. Data mining techniques can predict the rainfall through extracting the hidden patterns among weather attributes of past data. This research contributes by exploring the use of various data mining techniques for rainfall prediction in Lahore city. Techniques include: Support Vector Machine (SVM), Naïve Bayes (NB), k Nearest Neighbor (kNN), Decision Tree (J48) and Multilayer Perceptron (MLP). The dataset is obtained from a weather forecasting website and consists of several atmospheric attributes. For effective prediction, pre-processing technique is used which consists of cleaning and normalization processes. Performance of used data mining techniques is analyzed in terms of precision, recall and f-measure with various ratios of training and test data.

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Section: Methodsmentioning

confidence: 99%

Rainfall Prediction in Lahore City using Data Mining Techniques

Aftab¹,

Ahmad²,

Hameed³

et al. 2018

ijacsa

Self Cite

View full text Add to dashboard Cite

show abstract

“…Precision, recall and f-measure were used for performance evaluation. In [12], the authors have used SVM for sentiment analysis of twitter data. In this experiment, default parameters were selected in Weka along with 10 fold cross validation technique.…”

Section: Related Workmentioning

confidence: 99%

SVM Optimization for Sentiment Analysis

Ahmad¹,

Aftab²,

Bashir³

et al. 2018

ijacsa

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Abstract-Exponential growth in mobile technology and mini computing devices has led to a massive increment in social media users, who are continuously posting their views and comments about certain products and services, which are in their use. These views and comments can be extremely beneficial for the companies which are interested to know about the public opinion regarding their offered products or services. This type of public opinion otherwise can be obtained via questionnaires and surveys, which is no doubt a difficult and complex task. So, the valuable information in the form of comments and posts from micro-blogging sites can be used by the companies to eliminate the flaws and to improve the products or services according to customer needs. However, extracting a general opinion out of a staggering number of users' comments manually cannot be feasible. A solution to this is to use an automatic method for sentiment mining. Support Vector Machine (SVM) is one of the widely used classification techniques for polarity detection from textual data. This study proposes a technique to tune the SVM performance by using grid search method for sentiment analysis. In this paper, three datasets are used for the experiment and performance of proposed technique is evaluated using three information retrieval metrics: precision, recall and f-measure.

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“…Supervised machine learning techniques need the pre-classified data (training data) for training. During the training process these techniques make rules to classify the unseen data (test data) [18][19], [20][21][22][23], [26][27]. In this study, NASA's clean software defect datasets are used for experiments, including: CM1, JM1, KC1, KC3, MC1, MC2, MW1, PC1, PC2, PC3, PC4 and PC5.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

Iqbal¹,

Aftab²,

Ali³

et al. 2019

IJACSA

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Defect prediction at early stages of software development life cycle is a crucial activity of quality assurance process and has been broadly studied in the last two decades. The early prediction of defective modules in developing software can help the development team to utilize the available resources efficiently and effectively to deliver high quality software product in limited time. Until now, many researchers have developed defect prediction models by using machine learning and statistical techniques. Machine learning approach is an effective way to identify the defective modules, which works by extracting the hidden patterns among software attributes. In this study, several machine learning classification techniques are used to predict the software defects in twelve widely used NASA datasets. The classification techniques include: Naïve Bayes (NB), Multi-Layer Perceptron (MLP). Radial Basis Function (RBF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), kStar (K*), One Rule (OneR), PART, Decision Tree (DT), and Random Forest (RF). Performance of used classification techniques is evaluated by using various measures such as: Precision, Recall, F-Measure, Accuracy, MCC, and ROC Area. The detailed results in this research can be used as a baseline for other researches so that any claim regarding the improvement in prediction through any new technique, model or framework can be compared and verified.

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Sentiment Analysis of Tweets using SVM

Cited by 111 publications

References 18 publications

Rainfall Prediction in Lahore City using Data Mining Techniques

Rainfall Prediction in Lahore City using Data Mining Techniques

SVM Optimization for Sentiment Analysis

Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets

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