Sentiment Analysis on Bangladesh Cricket with Support Vector Machine

Mahtab, Shamsul Arafin; Islam, Nazmul; Rahaman, Md. Mahfuzur

doi:10.1109/icbslp.2018.8554585

Cited by 39 publications

(13 citation statements)

References 10 publications

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“…The use of applicable kernel functions can drastically reduce computational efforts to make operations feasible. Researchers have employed kernel of SVM in their studies, such as Polynomial Kernel (PK) [20]- [22], Radial Basis Function Kernel (RBF) [20]- [23], Gaussian Kernel [21], Linear Kernel [21], Sigmoid Kernel [21], Laplacian Kernel [21], and Anova Kernel [21], but there are only a few researchers applying Normalized Poly Kernel despite its excellent performance [24] [25]. Thus, in this research, we employed a Normalized Poly Kernel in Support Vector Machine (SVM)…”

Section: Introductionmentioning

confidence: 99%

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo

Sumi

Dian

et al. 2020

JISEBI

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Background: Handling COVID-19 (Corona Virus Disease-2019) in Indonesia was once trending on Twitter. The Indonesian government's handling evoked pros and cons in the community. Public opinions on Twitter can be used as a decision support system in making appropriate policies to evaluate government performance. A sentiment analysis method can be used to analyse public opinion on Twitter.Objective: This study aims to understand public opinion trends on COVID-19 in Indonesia both from a general perspective and an economic perspective.Methods: We used tweets from Twitterscraper library. Because they did not have a label, we provided labels using sentistrength_id and experts to be classified into positive, negative, and neutral sentiments. Then, we carried out a pre-processing to eliminate duplicate and irrelevant data. Next, we employed machine learning to predict the sentiments for new data. After that, the machine learning algorithms were evaluated using confusion matrix and K-fold cross-validation.Results: The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure with the value of 82.00%, 82.24%, 82.01%, and 81.84%, respectively.Conclusion: From the economic perspective, people seemed to agree with the government’s policies in dealing with COVID-19; but people were not satisfied with the government performance in general. The SVM algorithm with the Normalized Poly Kernel can be used as an intelligent algorithm to predict sentiment on Twitter for new data quickly and accurately.

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Section: Introductionmentioning

confidence: 99%

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Prastyo

Sumi

Dian

et al. 2020

JISEBI

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“…In Alshari et al [13] authors described SentiWordNet (SW) as a curse of dimensionality, they used sentimental lexicon dictionary based on word2vec to perform SA. Besides, in Bangla text, author [14] preprocessed data to carry through a SA by taking TF-IDF vectorizer and classified the data with support vector machine (SVM) algorithm, however they did not measure the polarity by calculating the score of a text; hence it is required to detect the polarity of each sentence by a specific rule-based [15] algorithm. In Chowdhury and Chowdhury [16], the author proposed a semi-supervised bootstrapping approach in SVM and maximum entropy (MaxEnt) classifier to perform a SA using SW by translating Bangla word to English.…”

Section: Related Workmentioning

confidence: 99%

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Bhowmik¹,

Arifuzzaman²,

Mondal³

et al. 2021

NLPRE

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With the proliferation of the Internet's social digital content, sentiment analysis (SA) has gained a wide research interest in natural language processing (NLP). A few significant research has been done in Bangla language domain because of having intricate grammatical structure on text. This paper focuses on SA in the context of Bangla language. Firstly, a specific domain-based categorical weighted lexicon data dictionary (LDD) is developed for analyzing sentiments in Bangla. This LDD is developed by applying the concepts of normalization, tokenization, and stemming to two Bangla datasets available in GitHub repository. Secondly, a novel rule-based algorithm termed as Bangla Text Sentiment Score (BTSC) is developed for detecting sentence polarity. This algorithm considers parts of speech tagger words and special characters to generate a score of a word and thus that of a sentence and a blog. The BTSC algorithm along with the LDD is applied to extract sentiments by generating scores of the two Bangla datasets. Thirdly, two feature matrices are developed by applying term frequency-inverse document frequency (tf-idf) to the two datasets, and by using the corresponding BTSC scores. Next, supervised machine learning classifiers are applied to the feature matrices. Results show that for the case of BiGram feature, support vector machine (SVM) achieves the best classification accuracy of 82.21% indicating the effectiveness of BTSC algorithm in Bangla SA.

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“…Many research works have been done in the area of sentiment analysis [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Our paper focuses on two things; keywords (Interactive Tag Cloud) and Sentiment Analysis(Positive, Negative and Neutral).…”

Section: Related Workmentioning

confidence: 99%

“…Xiaowen Ding, Bing Liu, and Philip S. Yu [7] did a research on "A holistic lexicon-based approach to opinion mining" in 2008 which primarily emphases on customer reviews of products and the problem of determining the semantic orientations associated with them such as positive or negative or neutral. Another study based on Machine Learning for sentiment classi cation was done by S. Ara n Mahtab, N. Islam and M. Mahfuzur Rahaman [10] on "Sentiment Analysis on Bangladesh Cricket with Support Vector Machine" in 2018 shows 64.59% average accuracy. Tho, Cuk, Harco Leslie Hendric Spits Warnars, Benfano Soewito, and Ford Lumban Gaol [13] did a study on this topic "Code-Mixed Sentiment Analysis Using Machine Learning Approach-A Systematic Literature Review" in 2020 which is focused on studying the approaches used in code-mixed sentiments analysis.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Social Media Sentiment Analysis: Machine Intelligence Model For Worldwide Covid-19 Vaccination Using Twitter Data

Jena

Bhoi

Behera

et al. 2021

Preprint

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Understanding human emotions is one of the crucial aspects when we are to take action. Our emotions dictate our apparent behaviors. In simple words, what we feel inside can predict things about what we would do. This creates a huge opportunity for government and businesses industry to understand and predict people's behaviors. There has been some really great research done on this with high accuracy. Recently, Covid-19 vaccination process is a challenging task going on all over the world and it is necessary to explore people’s reaction over this for more effective vaccination process spread. In this paper, wetried to understand an event (Covid-19 vaccination) with a relatively simple model with decent accuracy compared to other sophisticated models. We use simple machine learning models to train and deploy it over the network. We have used KNIME Analytical Platform to design and implement our model as it provides end-to-end analytics. We have managed to get 88.67% accuracy and Cohen’s kappa 0.789 with SVM model by tuning some parameters. The model is deployed on Twitter data. This paper shows our efforts trying to make a simple model to analyze an event (Covid-19 vaccination) and understand people's emotions towards the event. The methodology involves identifying important topics (terms) and finding out the sentiment (positive, negative, neutral). This paper tries to find a low-cost solution to analyze an event and provide data-driven insights from it without involving sophisticated algorithms.

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Sentiment Analysis on Bangladesh Cricket with Support Vector Machine

Cited by 39 publications

References 10 publications

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized Poly Kernel

Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

A Social Media Sentiment Analysis: Machine Intelligence Model For Worldwide Covid-19 Vaccination Using Twitter Data

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