SENTIMENT ANALYSIS OF MERDEKA BELAJAR KAMPUS MERDEKA POLICY USING SUPPORT VECTOR MACHINE WITH WORD2VEC

Sentiment analysis is a data text analysis that classifies data into positive and negative sentiments. This study aims to obtain the results of sentiment classification related to Merdeka Belajar Kampus Merdeka policy on Twitter using support vector machine algorithm with Word2Vec feature extraction. Support Vector Machine is a classification algorithm that separates data classes using the optimum hyperplane. Text data used in sentiment analysis must change its numerical form by performing feature extraction. In this study, the feature extraction used is Word2Vec which represents words in vector form. Data in this study are tweets with the keyword "Kampus Merdeka" uploaded on Twitter as many as 10000 tweets. After preprocessing text data, data used to analyze sentiment was 1579 tweets. Sentiment classification resulted in classification model accuracy 89.87%, precision 91.20%, recall 84.44% and F-Measure 87.68%. Classification sentiment using support vector machine with Word2Vec feature extraction in this study produces a good model

TWITTER SENTIMENT ANALYSIS PEDULILINDUNGI APPLICATION USING NAÏVE BAYES AND SUPPORT VECTOR MACHINE

The PeduliLindungi application is an application launched by the government during the COVID-19 pandemic, with the aim of helping government agencies carry out digital tracking to monitor the public, as an effort to prevent the spread of the Corona virus. Many people express their opinions on the PeduliLindung application on social media, one of which is through Twitter. To improve the performance of the application, of course, need input or complaints from users, opinions from the public on Twitter about the PeduliLindungi application can be input to improve or improve the performance of the application. Sentiment analysis is carried out to see how the public's sentiment towards the PeduliLindung application is, and these sentiments will be categorized into positive sentiment and negative sentiment, this sentiment can later be used as evaluation material for application development. This study aims to see and compare the accuracy of two classification methods, Naïve Bayes and Support Vector Machine in the classification process of sentiment analysis. The data used are 4636 tweets with the keyword " PeduliLindungi". The data obtained then goes to the pre-processing stage before going to the classification stage. The results obtained after classifying using the Naïve Bayes method and the Support Vector Machine show that the Support Vector Machine method has a higher accuracy of 91%, while the Naïve Bayes method has an accuracy of 90%

Analisis Sentiment Menggunakan Dictionary Based Approach dan Support Vector Machine Method

ABSTRAKSI: Internet atau interconnected network adalah sebuah sistem komunikasi global yang menghubungkan komputer-komputer dan jaringan komputer di seluruh dunia dan merupakan sebuah teknologi yang berkembang sangat pesat. Banyak sekali manfaat dan kegunaan yang didapat dari internet dan contohnya adalah untuk media sosial maupun media bisnis. Twitter adalah salah satu media sosial yang terkenal di dunia maya. Penggunaan twitter meluas ke dalam berbagai bidang dalam masyarakat. Banyak orang maupun golongan memanfaatkan twitter sebagai sebuah sarana untuk berbisnis, untuk customer relationship, maupun untuk kegiatan lainnya. Oleh karena itu, sebagian besar data tweet yang ada pada twitter tersebut berupa kalimat opini. Masalahnya adalah dengan semakin meluasnya penggunaan twitter untuk tujuan tersebut, maka dibutuhkan sebuah cara untuk menganalisis kalimat-kalimat opini secara efektif dan efisien atau biasa disebut dengan sentiment analysis pada twiter tersebut. Dalam Tugas Akhir ini penulis menggunakan metode Dictionary Based Approach dan Support Vector Machine untuk menyelesaikan permasalahan yang ada dengan mengklasifikasikan tweet-tweet yang berupa kalimat opini pada twitter dengan menggunakan kedua metode tersebut. Namun demikian, kehandalan Dictionary Based Approach dan Support Vector Machine dalam melakukan analisis sentiment bergantung pada banyak faktor. Beberapa faktor diantaranya adalah pengaruh stemming pada proses preprocessing terhadap kedua metode, parameter C pada metode Support Vector Machine, dan pengaruh komposisi data pada data latih untuk metode Support Vector Machine. Hasil evaluasi eksperimental yang telah dilakukan menunjukan bahwa metode Dictionary Based Approach dan Support Vector Machine mampu menyelesaikan permasalahan sentimet analysis. Hasil evaluasi eksperimental juga menunjukan bahwa proses stemming dan komposisi data pada data latih dapat mempengaruhi hasil klasifikasi sentiment, sedangkan nilai parameter C tidak dapat mempengaruhi hasil klasifikasi secara signifikan.Kata Kunci : Twitter, Sentiment Analysis, Dictionary Based Approach, Support Vector Machine, Stemming, Parameter C, Komposisi Data, PerformansiABSTRACT: Internet or interconnected network is a global communications system taht links computers and computer networks worldwide and it is a technology that is growing very fast. There are so many benefits and uses can be obtained from the internet and the examples are for social media and business media. Twitter is one of the famous social networking in cyberspace. The use of twitter extends into the various fields in society. Many individuals or groups make use of twitter as a means to do business, customer relationship, and for other activities. Therefore, most of the existing data tweet on twitter is an opinion sentence. The problem is with the increasingly widespread use of twitter for that purpose, it needed a way to analyze the opinion sentences effectively and efficiently or commonly known as sentiment analysis on twiter. In this Final Assignment, writer use Dictionary Based Approach and Support Vector Machine method to solve existing problems by classifying opinion sentence tweets on twitter by using both methods. However, the reliability of Dictionary Based Approach and Support Vector Machine in sentiment analysis depends on many factors. Several factors including the effect of stemming on the preprocessing of both methods, the C parameter on the Support Vector Machine method, and the influence of data distribution of the training data for the Support Vector Machine method. The result of experimental evaluation that has been done, shows that the Dictionary Based Approach and Support Vector Machine method is able to solve the sentiment analysis problmes. The result of experimental evaluation also shows tahat the stemming process and the distribution of data in data trainning can affect the outcome of sentiment classification, while the value of C parameter can’t affect the outcome of sentiment classification significantly.Keyword: Twitter, Sentiment Analysis, Dictionary Based Approach, Support Vector Machine, Stemming, C Parameter, Data Composition, Performanc

SVM-PSO Algorithm for Tweet Sentiment Analysis #BesokSenin

The hashtag #BesokSenin is a hashtag that is often trending on Indonesian Twitter on Sunday evenings. Many Indonesian Twitter users expressed their feelings about welcoming Monday using the hashtag #BesokSenin. The tweet containing #BesokSenin is known to be a motivational sentence to welcome Monday full of joy or a disappointed sentence because you have to return to your routine after taking a holiday on Saturday and Sunday. This study conducts sentiment analysis to find out the opinions of netizens on welcoming Mondays. The tweet data used is tweet data with the hashtag #BesokSenin and the keywords school, work, assignments, and college. The classification method used is the Support Vector Machine algorithm, which is optimized using the Particle Swarm Optimization method to optimize the performance of the Support Vector Machine algorithm. Results of 80% accuracy were obtained by applying the Support Vector Machine model based on Particle Swarm Optimization. This accuracy is superior to 1% compared to the results of accuracy using the usual Support Vector Machine model, which equals 79%. This shows that Particle Swarm  Optimization can optimize the accuracy of the Support Vector Machine algorithm

Sentiment Analysis from Indonesian Twitter Data Using Support Vector Machine And Query Expansion Ranking

Sentiment analysis is a computational study of a sentiment opinion and an overflow of feelings expressed in textual form. Twitter has become a popular social network among Indonesians. As a public figure running for president of Indonesia, public opinion is very important to see and consider the popularity of a presidential candidate. Media has become one of the important tools used to increase electability. However, it is not easy to analyze sentiments from tweets on Twitter apps, because it contains unstructured text, especially Indonesian text. The purpose of this research is to classify Indonesian twitter data into positive and negative sentiments polarity using Support Vector Machine and Query Expansion Ranking so that the information contained therein can be extracted and from the observed data can provide useful information for those in need. Several stages in the research include Crawling Data, Data Preprocessing, Term Frequency – Inverse Document Frequency (TF-IDF), Feature Selection Query Expansion Ranking, and data classification using the Support Vector Machine (SVM) method. To find out the performance of this classification process, it will be entered into a configuration matrix. By using a discussion matrix, the results show that calcification using the proposed reached accuracy and F-measure score in 77% and 68% respectively

Enhancing the Sentiment Classification Accuracy of Twitter Data using Machine Learning Algorithms

Sentiment analysis or opinion mining is the study of public opinions, sentiments, attitudes, and emotions expressed in social media. This is one of the most dynamic research areas in natural language processing and text mining in current years. It is a domain that involves the finding of user sentiment, emotion and opinion within natural language text. The growing significance of sentiment analysis coincides with the increase of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. Common applications of sentiment analysis include the automatic determination of whether a review posted online (of a movie, a book, or a consumer product) is positive or negative toward the item being reviewed. This research work shows the various pathways to perform a computational treatment of sentiments and opinions. The main aim of this work is to classify the sentiment of twitter data using machine learning algorithms. The sentiment classifications have been classified into two types which are emotional classification and polarity classification. This work has been carried out on polarity classification, which is used to classify the text such as positive, negative, and neutral. The polarity classification is done by using the subjectivity lexicon. After the polarity classification two machine learning algorithms are employed to enhance the accuracy of sentiment classification. In the Pre-processing phase, the tweets are preprocessed by using various techniques. Sentiment classification is the essential phase, where preprocessed tweets are taken as input to sentiment classification. The sentiment classification can be done by using subjectivity lexicon. The third phase of the proposed work is to compare and evaluate the performance of two machine learning algorithms which are Support Vector Machine and Decision tre

SENTIMENT ANALYSIS OF POST-COVID-19 INFLATION BASED ON TWITTER USING THE K-NEAREST NEIGHBOR AND SUPPORT VECTOR MACHINE CLASSIFICATION METHODS

The COVID-19 pandemic caused a crisis in global economic growth. The impact of injuries due to the COVID-19 pandemic has also caused price increases and an increase in the inflation rate. Inflation is a price increase caused by a certain factor so that it has an impact on the prices of nearby goods which increase the circulation of money in society to increase. Many people expressed their various opinions or criticisms of the post-COVID-19 price increase policy on social media, one of which was via Twitter. Sentiment analysis was carried out to see how public sentiment is towards the price increase policy after the COVID-19 pandemic, and these sentiments are combined into multiclasses, namely positive, negative and neutral sentiments. So that this sentiment can later be used as material for evaluation regarding the post-COVID-19 price increase policy. This study aims to see and compare the accuracy of the two classification methods, namely K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) in the sentiment classification process. The data used was 5989 tweets with the keywords ""Stuffets Go Up Post-Pandemic", "Fuel Goes Up", "Inflation 2022", "Covid19 Inflation", "Inflation Post-Pandemic" with a data collection period from August to October 2022. The data obtained then enter the text preprocessing stage before later entering the classification stage. The results obtained after carrying out the classification using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM) methods show that the Support Vector Machine (SVM) method has a higher accuracy of 79%, while the K-Nearest Neighbor (K -NN) has an accuracy of 54%

NEW INDONESIA CAPITAL SENTIMENT ANALYSIS ON TWITTER USING SVM AND APPROACH WITH LEXICON METHOD

Social media is growing fast on the internet. One of the most popular social media is Twitter. Many topics are discussed on Twitter such as economic, politic, social, culture, and law. One of the hot topics discussed on Twitter is the issue of relocating Indonesia's capital city. However, there is controversy from supporters and opponents. They have different views. This issue leads to a phenomenon of debate on Twitter that actually shows a collective concern about the public discourse. Sentiment analysis is a process of extracting, understanding and processing unstructured data to get sentiment information which is found in an opinion sentence. Application of sentiment analysis using machine learning methods shows that there are several method that are often used in In this study, the Support Vector Machine (SVM) method is proposed to be applied to classified sentiment tweets on the topic of Indonesia new capital on social media twitter. The classification technique is carried out into 3 classes, namely positive and negative, and neutral. Before the classification process the data is labeled by lexicon method approach to help increase accuracy. This research also use K-Fold Cross Validation for Evaluation and Validation the classification model. Based on testing on the sentiment of Indonesia new capital city from social media twitter from 3000 tweets (1674 positive, 1005, and 321 neutral) using SVM with lexicon method approach obtained accuracy 86%. After evaluation dan validation using K-Fold Cross Validation the accuracy increase from 87% to 88,23%

Performance analysis of sentiments in Twitter dataset using SVM models

Sentiment Analysis is a current research topic by many researches using supervised and machine learning algorithms. The analysis can be done on movie reviews, twitter reviews, online product reviews, blogs, discussion forums, Myspace comments and social networks. The Twitter data set is analyzed using support vector machines (SVM) classifier with various parameters. The content of tweet is classified to find whether it contains fact data or opinion data. The deep analysis is required to find the opinion of the tweets posted by the individual. The sentiment is classified in to positive, negative and neutral. From this classification and analysis, an important decision can be made to improve the productivity. The performance of SVM radial kernel, SVM linear grid and SVM radial grid was compared and found that SVM linear grid performs better than other SVM models

Analisis Sentiment Twitter Berbasis Grid Search Algorithm (GSA) Dengan Metode Support Vector Machine (SVM)

Twitter is a social networking service that has undergone tremendous growth and is gaining worldwide popularity at an accelerated rate. Twitter allows for the expression of unbiased thoughts on a variety of issues and can assist businesses in providing public feedback on well-known brands and items. Twitter is having trouble with good and negative answers. Researchers evaluated English-language tweets to determine the proportion of positive and negative replies to popular companies and items. This study will explore Twitter sentiment analysis utilizing the Grid Search Algorithm (GSA) and the support vector machine (SVM) technique. GSA is utilized by the feature selection model to optimize the classification procedure. In this work, training data and testing data are required to do sentiment analysis. Sanders Twitter 0.2 utilizes a dataset consisting of tweets retrieved from Twitter using the search terms @apple, #google, #microsoft, and #twitter. The collected dataset was manually annotated and included 654 negatives, 570 positives, 2503 neutrals, and 1786 irrelevant entries. Data are loaded, tokenized, weighted, preprocessed, filtered, and classified to conduct a sentiment analysis. The application's sentiment analysis achieved a degree of accuracy of up to 79% based on testing. The ratio of neutral and bad tweets on data sandboxes tends to be greater than the percentage of positive tweets, hence optimization rather than accuracy is obtained