Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review

Sentiment analysis (SA) is the automated process of detecting and understanding the emotions conveyed through written text. Over the past decade, SA has gained significant popularity in the field of Natural Language Processing (NLP). With the widespread use of social media and online platforms, SA has become crucial for companies to gather customer feedback and shape their marketing strategies. Additionally, researchers rely on SA to analyze public sentiment on various topics. In this particular research study, a comprehensive survey was conducted to explore the latest trends and techniques in SA. The survey encompassed a wide range of methods, including lexicon-based, graph-based, network-based, machine learning, deep learning, ensemble-based, rule-based, and hybrid techniques. The paper also addresses the challenges and opportunities in SA, such as dealing with sarcasm and irony, analyzing multi-lingual data, and addressing ethical concerns. To provide a practical case study, Twitter was chosen as one of the largest online social media platforms. Furthermore, the researchers shed light on the diverse application areas of SA, including social media, healthcare, marketing, finance, and politics. The paper also presents a comparative and comprehensive analysis of existing trends and techniques, datasets, and evaluation metrics. The ultimate goal is to offer researchers and practitioners a systematic review of SA techniques, identify existing gaps, and suggest possible improvements. This study aims to enhance the efficiency and accuracy of SA processes, leading to smoother and error-free outcomes

Senti-Lexicon and Analysis for Restaurant Reviews of Myanmar Text

Social media has just become as an influential with the rapidly growing popularity of online customers reviews available in social sites by using informal languages and emoticons. These reviews are very helpful for new customers and for decision making process. Sentiment analysis is to state the feelings, opinions about people\u27s reviews together with sentiment. Most of researchers applied sentiment analysis for English Language. There is no research efforts have sought to provide sentiment analysis of Myanmar text. To tackle this problem, we propose the resource of Myanmar Language for mining food and restaurants\u27 reviews. This paper aims to build language resource to overcome the language specific problem and opinion word extraction for Myanmar text reviews of consumers. We address dictionary based approach of lexicon-based sentiment analysis for analysis of opinion word extraction in food and restaurants domain. This research assesses the challenges and problem faced in sentiment analysis of Myanmar Language area for future

Integrasi Metode Information Gain untuk Seleksi Fitur dan AdaBoost untuk Mengurangi Bias pada Analisis Sentimen Review Restoran Menggunakan Algoritma Naive Bayes

Internet merupakan bagian penting dari kehidupan sehari-hari. Saat ini, tidak hanya dari anggota keluarga dan teman-teman, tetapi juga dari orang asing yang berlokasi diseluruh dunia yang mungkin telah mengunjungi restoran tertentu. Konsumen dapat memberikan pendapat mereka yang sudah tersedia secara online. Ulasan yang terlalu banyak akan memakan banyak waktu dan pada akhirnya akan menjadi bias. Klasifikasi sentimen bertujuan untuk mengatasi masalah ini dengan cara mengklasifikasikan ulasan pengguna ke pendapat positif atau negatif. Pengklasifikasi Naive Bayes adalah tekhnik machine learning yang populer untuk klasifikasi teks, karena sangat sederhana, efisien dan memiliki performa yang baik pada banyak domain. Namun, Naive Bayes memiliki kekurangan yaitu sangat sensitif pada fitur yang terlalu banyak, sehingga membuat akurasi menjadi rendah. Oleh karena itu, dalam penelitian ini menggunakan Information Gain sebagai seleksi fitur dan metode AdaBoost untuk mengurangi bias agar dapat meningkatkan akurasi pengklasifikasi Naive Bayes. Penelitian ini menghasilkan klasifikasi teks dalam bentuk positif dan negatif dari review restoran. Pengukuran berdasarkan akurasi Naive Bayes sebelum dan sesudah penambahan metode pemilihan fitur. Validasi dilakukan dengan menggunakan 10 fold cross validation. Sedangkan pengukuran akurasi diukur dengan confusion matrix dan kurva ROC. Hasil penelitian menunjukkan peningkatan akurasi Naive Bayes dari 73.00% jadi 81.50% dan nilai AUC dari 0.500 jadi 0.887. Sehingga dapat disimpulkan bahwa integrasi metode Information Gain dan AdaBoost pada analisis sentimen review restoran ini mampu meningkatkan akurasi algoritma Naive Bayes

A Review on Opinion Mining: Approaches, Practices and Application

Opinion Mining also known as Sentiment Analysis (SA) has recently become the focus of many researchers, because analysis of online text is useful and demanded in many different applications. Analysis of social sentiments is a trending topic in this era because users share their emotions in more suitable format with the help of micro blogging services like twitter. Twitter provides information about individual's real-time feelings through the data resources provided by persons. The essential task is to extract user's tweets and implement an analysis and survey. However, this extracted information can very helpful to make prediction about the user's opinion towards specific policies. The motive of this paper is to perform a survey on sentiment analysis algorithms that shows the utilizing of different ML and Lexicon investigation methodologies and their accuracy. Our paper also focuses on the three kinds of machine learning algorithms for Sentiment Analysis- Supervised, Unsupervised Algorithms

Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets

Sentiment analysis aims to extract people's emotions and opinion from their comments on the web. It widely used in businesses to detect sentiment in social data, gauge brand reputation, and understand customers. Most of articles in this area have concentrated on the English language whereas there are limited resources for Persian language. In this review paper, recent published articles between 2018 and 2022 in sentiment analysis in Persian Language have been collected and their methods, approach and dataset will be explained and analyzed. Almost all the methods used to solve sentiment analysis are machine learning and deep learning. The purpose of this paper is to examine 40 different approach sentiment analysis in the Persian Language, analysis datasets along with the accuracy of the algorithms applied to them and also review strengths and weaknesses of each. Among all the methods, transformers such as BERT and RNN Neural Networks such as LSTM and Bi-LSTM have achieved higher accuracy in the sentiment analysis. In addition to the methods and approaches, the datasets reviewed are listed between 2018 and 2022 and information about each dataset and its details are provided

The Today Tendency of Sentiment Classification

Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activities, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details

A Context-Dependent Sentiment Analysis of Online Product Reviews based on Dependency Relationships

Consumers often view online consumer product review as a main channel for obtaining product quality information. Existing studies on product review sentiment analysis usually focus on identifying sentiments of individual reviews as a whole, which may not be effective and helpful for consumers when purchase decisions depend on specific features of products. This study proposes a new feature-level sentiment analysis approach for online product reviews. The proposed method uses an extended PageRank algorithm to extract product features and construct expandable context-dependent sentiment lexicons. Moreover, consumers’ sentiment inclinations toward product features expressed in each review can be derived based on term dependency relationships. The empirical evaluation using consumer reviews of two different products shows a higher level of effectiveness of the proposed method for sentiment analysis in comparison to two existing methods. This study provides new research and practical insights on the analysis of online consumer product reviews

Komparasi Algoritma Klasifikasi Machine Learning dan Feature Selection pada Analisis Sentimen Review Film

Analisis sentimen adalah proses yang bertujuan untuk menentukan isi dari dataset yang berbentuk teks bersifat positif, negatif atau netral. Saat ini, pendapat khalayak umum menjadi sumber yang penting dalam pengambilan keputusan seseorang akan suatu produk. Algoritma klasifikasi seperti Naïve Bayes (NB), Support Vector Machine (SVM), dan Artificial Neural Network (ANN) diusulkan oleh banyak peneliti untuk digunakan pada analisis sentimen review film. Namun, klasifikasi sentimen teks mempunyai masalah pada banyaknya atribut yang digunakan pada sebuah dataset. Feature selection dapat digunakan untuk mengurangi atribut yang kurang relevan pada dataset. Beberapa algoritma feature selection yang digunakan adalah information gain, chi square, forward selection dan backward elimination. Hasil komparasi algoritma, SVM mendapatkan hasil yang terbaik dengan accuracy 81.10% dan AUC 0.904. Hasil dari komparasi feature selection, information gain mendapatkan hasil yang paling baik dengan average accuracy 84.57% dan average AUC 0.899. Hasil integrasi algoritma klasifikasi terbaik dan algoritma feature selection terbaik menghasilkan accuracy 81.50% dan AUC 0.929. Hasil ini mengalami kenaikan jika dibandingkan hasil eksperimen yang menggunakan SVM tanpa feature selection. Hasil dari pengujian algoritma feature selection terbaik untuk setiap algoritma klasifikasi adalah information gain mendapatkan hasil terbaik untuk digunakan pada algoritma NB, SVM dan ANN