Greetings, fellow Nawala! May you always be in good health.
This is the IAES Nawala of the Institute of Advanced Engineering and Science. Today we will share some news about sentiment analysis. Sentiment analysis is a natural language processing (NLP) technique used to identify sentiments or feelings reflected in text. The goal is to categorize the feelings reflected in the text as positive, negative, or neutral sentiment. Sentiment analysis is used in many applications, including social media monitoring, product reviews, and market research. In sentiment analysis, computers are used to classify text based on the keywords and context used. Samah et al. (2023) conducted research on a hospital in Malaysia. They calcified reviews about the hospital whose data was taken from Twitter, and then created a visualization of the results of the sentiment analysis conducted.
Classification and visualization: Twitter sentiment analysis of Malaysia’s private hospitals
Khyrina Airin Fariza Abu Samah, Nur Maisarah Nor Azharludin, Lala Septem Riza, Mohd Nor Hajar Hasrol Jono, Nor Aiza Moketar
Malaysia has many private’s hospitals. Thus, feedback is important to improve service quality, becoming reviews for other patients. Reviews use the channel service provided on social media, such as Twitter. Nevertheless, online reviews are unstructured and enormous in volume, which leads to difficulties in comparing private hospitals. In addition, no single websites compare private hospitals based on users’ interests, bilingual reviews, and less time-consuming. Due to that, this study aims to classify and visualize the Twitter sentiment analysis of private hospitals in Malaysia. The scope focuses on five factors: 1) administrative procedure, 2) cost, 3) communication, 4) expertise, and 5) service. Term frequency-inverse document frequency is used for text mining, information retrieval techniques, and the Naïve Bayes, a machine learning algorithm for the classification. The user can visualize the specified state’s private hospitals and compare them with any selected state. The system’s functionality and usability have been tested to ensure it meets the objectives. Functionality testing proved that the private hospital’s Twitter sentiment could be predicted based on the training and testing data as intended, with 77.13% and 77.96% accuracy for English and Bahasa Melayu, respectively, while the system usability scale based on the usability testing resulted in an average final score of 95.42%.
In today’s social media and internet age, sentiment analysis is becoming increasingly important as it gives businesses the ability to monitor public sentiment about their products and brands in real-time. Sentiment analysis and machine learning are intertwined, as machine learning can be effectively used to perform sentiment analysis. Using machine learning techniques, text or speech data can be classified into different sentiment categories, such as positive, negative, or neutral. Machine learning-based sentiment analysis can be applied to various domains such as social media, customer reviews, and news articles, which helps organizations make data-driven decisions and gain insights into customer opinions and trends. Shah et al. (2022) used machine learning to analyze movie reviews in Gujarati language. More results related to the research can be seen in the article below.
Sentiment analysis on film review in Gujarati language using machine learning
Parita Shah, Priya Swaminarayan, Maitri Patel
Opinion analysis is by a long shot most basic zone of characteristic language handling. It manages the portrayal of information to choose the motivation behind the wellspring of the content. The reason might be of a type of gratefulness (positive) or study (negative). This paper offers a correlation between the outcomes accomplished by applying the calculation arrangement using various classifiers for instance K-nearest neighbor and multinomial naive Bayes. These techniques are utilized to assess a significant assessment with either a positive remark or negative remark. The gathered information considered on the grounds of the extremity film datasets and an association with the results accessible proof has been created for a careful assessment. This paper investigates the word level count vectorizer and term frequency inverse document frequency (TF-IDF) influence on film sentiment analysis. We concluded that multinomial Naive Bayes (MNB) classier generate more accurate result using TF-IDF vectorizer compared to CountVectorizer, K-nearest-neighbors (KNN) classifier has the same accuracy result in case of TF-IDF and CountVectorizer.
Guha and Sutikno (2022) combined sentiment analysis and deep learning. Sentiment analysis and deep learning are closely related, as deep learning techniques have significantly improved the performance and accuracy of sentiment analysis. Deep learning has revolutionized the field of NLP and has become a cutting-edge approach to many NLP tasks, including sentiment analysis.
Natural language understanding challenges for sentiment analysis tasks and deep learning solutions
Radha Guha, Tole Sutikno
When it comes to purchasing a product or attending an event, most people want to know what others think about it first. To construct a recommendation system, a user’s likeness of a product can be measured numerically, such as a five-star rating or a binary like or dislike rating. If you don’t have a numerical rating system, the product review text can still be used to make recommendations. Natural language comprehension is a branch of computer science that aims to make machines capable of natural language understanding (NLU). Negative, neutral, or positive sentiment analysis (SA) or opinion mining (OM) is an algorithmic method for automatically determining the polarity of comments and reviews based on their content. Emotional intelligence relies on text categorization to work. In the age of big data, there are countless ways to use sentiment analysis, yet SA remains a challenge. As a result of its enormous importance, sentiment analysis is a hotly debated topic in the commercial world as well as academic circles. When it comes to sentiment analysis tasks and text categorization, classical machine learning and newer deep learning algorithms are at the cutting edge of current technology.
Sentiment analysis, machine learning, and deep learning are interconnected in the context of NLP tasks, and they represent different approaches to addressing the problem of understanding and categorizing sentiment in text data. In short, sentiment analysis is a valuable NLP task for understanding and categorizing sentiment in text. Machine learning and deep learning approaches have their respective strengths and can be applied depending on the scale of the data, computing resources, and the desired level of performance and interpretability. Deep learning, with its ability to capture complex patterns, has become particularly effective in recent years pushing the boundaries of sentiment analysis accuracy. Bitto et al. (2023) combined machine learning and deep learning to analyze the sentiment of food delivery startup reviews.
Abu Kowshir Bitto, Md. Hasan Imam Bijoy, Md. Shohel Arman, Imran Mahmud, Aka Das, Joy Majumder
Food delivery methods are at the top of the list in today’s world. People’s attitudes toward food delivery systems are usually influenced by food quality and delivery time. We did a sentiment analysis of consumer comments on the Facebook pages of Food Panda, HungryNaki, Pathao Food, and Shohoz Food, and data was acquired from these four sites’ remarks. In natural language processing (NLP) task, before the model was implemented, we went through a rigorous data pre-processing process that included stages like adding contractions, removing stop words, tokenizing, and more. Four supervised classification techniques are used: extreme gradient boosting (XGB), random forest classifier (RFC), decision tree classifier (DTC), and multi nominal Naive Bayes (MNB). Three deep learning (DL) models are used: convolutional neural network (CNN), long term short memory (LSTM), and recurrent neural network (RNN). The XGB model exceeds all four machine learning (ML) algorithms with an accuracy of 89.64%. LSTM has the highest accuracy rate of the three DL algorithms, with an accuracy of 91.07%. Among ML and DL models, LSTM DL takes the lead to predict the sentiment.
The above articles are a small part of the research on sentiment analysis. To get more information, readers can visit the page and read articles for FREE through the following links: https://ijai.iaescore.com/, https://ijece.iaescore.com/, https://ijict.iaescore.com/, and https://beei.org/.
By: I. Busthomi