Sentiment Predictions using Support Vector Machines for Odd-Even Formula in Delhi

Sharma, Sudhir Kumar; Hoque, Ximi

doi:10.5815/ijisa.2017.07.07

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…In Table 4 and 5, the class label corresponding to number 1 was positive, 2 was negative and 3, neutral. Various evaluation parameters were obtained such as true positive rate (TPR), false positive rate (FPR), Precision (% of selected items that are correct), Recall (% of correct items that are selected) [24] and ROC (receiver operating characteristic curve) [35] etc. Figure 3 and Figure 4 illustrates the Error measure comparison and Fmeasure for various classifiers in WEKA [33].…”

Section: Resultsmentioning

confidence: 99%

Aspect Sentiment Identification using Random Fourier Features

Thara¹,

S²

2018

IJISA

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The objective of the paper was to show the effectiveness of using random Fourier features in detection of sentiment polarities. The method presented in this paper proves that detection of aspect based polarities can be improved by selective choice of relevant features and mapping them to lower dimensions. In this study, random Fourier features were prepared corresponding to the polarity data. A regularized least square strategy was adopted to fit a model and perform the task of polarity detection Experiments were performed with 10 cross-validations. The proposed method with random Fourier features yielded 90% accuracy over conventional classifiers. Precision, Recall, and F-measure were deployed in our empirical evaluations.

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Section: Resultsmentioning

confidence: 99%

Aspect Sentiment Identification using Random Fourier Features

Thara¹,

S²

2018

IJISA

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“…The process of classification algorithms utilized in this study is explained below. The process is sourced and mapped from the study of Sharma and Hoque [15]:  Extract the relevant tweets from the obtained datasets.  Given a set of tweets, estimate tweets' distribution and classify them into positive, negative, and neutral classes based on terrorism content.…”

Section: A Classification Algorithmsmentioning

confidence: 99%

An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

AL-Harbi¹,

Kamsin²

2021

IJITCS

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Terrorist groups in the Arab world are using social networking sites like Twitter and Facebook to rapidly spread terror for the past few years. Detection and suspension of such accounts is a way to control the menace to some extent. This research is aimed at building an effective text classifier, using machine learning to identify the polarity of the tweets automatically. Five classifiers were chosen, which are AdB_SAMME, AdB_SAMME.R, Linear SVM, NB, and LR. These classifiers were applied on three features namely S1 (one word, unigram), S2 (word pair, bigram), and S3 (word triplet, trigram). All five classifiers evaluated samples S1, S2, and S3 in 346 preprocessed tweets. Feature extraction process utilized one of the most widely applied weighing schemes tf-idf (term frequency-inverse document frequency).The results were validated by four experts in Arabic language (three teachers and an educational supervisor in Saudi Arabia) through a questionnaire. The study found that the Linear SVM classifier yielded the best results of 99.7 % classification accuracy on S3 among all the other classifiers used. When both classification accuracy and time were considered, the NB classifier demonstrated the performance on S1 with 99.4% accuracy, which was comparable with Linear SVM. The Arab world has faced massive terrorist attacks in the past, and therefore, the research is highly significant and relevant due to its specific focus on detecting terrorism messages in Arabic. The state-of-the-art methods developed so far for tweets classification are mostly focused on analyzing English text, and hence, there was a dire need for devising machine learning algorithms for detecting Arabic terrorism messages. The innovative aspect of the model presented in the current study is that the five best classifiers were selected and applied on three language models S1, S2, and S3. The comparative analysis based on classification accuracy and time constraints proposed the best classifiers for sentiment analysis in the Arabic language.

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“…Furthermore, in [24] they built IQAS that has the ability to process queries in the form of factoid and non-factoid question. If the question was a factoid question, it used Named Entity Recognition (NER) and SVM [50][51] to process it. Vice versa, pattern matching and semantic analysis methods were used for non-factoid question.…”

Section: State Of the Art In Indonesian Questionmentioning

confidence: 99%

Survey on Answer Validation for Indonesian Question Answering System (IQAS)

Abdiansah¹,

Azhari²,

Sari³

2018

IJISA

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Abstract-Research on Question Answering System (QAS) has been done mainly in English. Unfortunately, for Indonesian, it is still rarely explored whereas Indonesian is the official language used more than 250 million people. Research in the area of Indonesian Question Answering System (IQAS) began in 2005s, and since then only few number of IQAS have been developed. One of the important issues in IQAS is Answer Validation (AV), which is a system that can determine the correctness of QAS. To identify the future scope of research in this area, the need of comprehensive survey on IQAS and AV arises naturally. The goals of this survey are to find the cutting-edge method used in AV and to prove that AV has not been implemented on IQAS. Based on the results, we suggest new opportunities and research challenges for IQAS community.

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Sentiment Predictions using Support Vector Machines for Odd-Even Formula in Delhi

Cited by 11 publications

References 9 publications

Aspect Sentiment Identification using Random Fourier Features

Aspect Sentiment Identification using Random Fourier Features

An Effective Text Classifier using Machine Learning for Identifying Tweets’ Polarity Concerning Terrorist Connotation

Survey on Answer Validation for Indonesian Question Answering System (IQAS)

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