Abstract
Objective: To examine the sounds in Thai videos on YouTube, to analyze consumer opinion on beauty products. Statistical Analysis: The data was collected to analyze the sentiments of Thai sounds from 500 YouTube videos which were the reviews of beauty products; the length of each video being approximately 2-5 minutes. Findings: The accuracy rate of SVM (SVM) appears greater than those from the Naïve Bayes (NB) and K-Nearest Neighbor (KNN) techniques. The SVM used the RBF Kernel-typed Sequential Minimal Optimization (SMO) function, where c=50000 and gamma=0.1; the accuracy rate was 94.40%, when using K-fold Cross Validation, where K =10, that had 293 attributes. Application/Improvement: The SVM used the RBF Kernel-typed SMO function, where c=50000 and gamma=0.1; it can be applied to analysis of sentiments studies in social media derived from Thai videos which are evaluate processes need to be fast and able to provide negative, neutral, or positive results in a timely manner, in which it will become a purchase decision-making guide for consumers. Keywords: Sentiments Analysis, Social Media, Support Vector Machine (SVM), Thai Sound, Video
Highlights
Social media is a prevalent communication channel for exchanging information, opinions, attitudes, and so on
The findings revealed that the Support Vector Machine (SVM) technique had the highest accuracy rate with 69.15%9 classified emotions towards the Hurricane Sandy hashtag on Twitter into 4 types consisting of positive, anger, fear, and other
Document 1 used the Term Frequency (TF) method for word substitution which gave out 293 attributes of adjectives
Summary
Social media is a prevalent communication channel for exchanging information, opinions, attitudes, and so on. Users can share information in different forms such as messages, pictures, sound audios and videos. Customers are able to instantly share their impressions and experiences of a product or service via social media which serves as an intermediary to rapidly distribute information to others by using common electronic devices including smartphones, tablets, and computers, in the use of popular social media applications, including Facebook, Twitter, and YouTube[1]. It is common for people to provide an opinion, or product review, in the form of a video on YouTube with the video that customers share from their experiences being regarded as an important piece of information for potential customers. There are a number of researchers that analyzed sentiments in communications (including sounds and images embedded in online videos). Most Thai sentiment analysis researchers mainly use texts written or posted about interesting topics such as products, services, and politics[2]
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