Sentiment analysis on online social network

A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis

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

Implicit Sentiment Identification using Aspect based Opinion Mining

Opinion mining or sentiment analysis is the computational study of opinions or emotions towards aspects or things. The aspects are nothing but attributes or components of the individuals, events, topics, products and organizations. Opinion mining has been an active research area in Web mining and Natural Language Processing (NLP) in recent years. With the explosive growth of E-commerce, there are millions of product options available and people tend to review the viewpoint of others before buying a product. An aspect-based opinion mining approach helps in analyzing opinions about product features and attributes. This project is based on extracting aspects and related customer sentiments on tourism domain. This offers an approach to discover consumer preferences about tourism products and services using statistical opinion mining. The proposed system tries to extract both explicit aspects as well as implicit aspects from customer reviews. It thus increases the sentiment orientation of opinion. Most of the researches were based on explicit opinions of customers. This system tries to retrieve implicit sentiments. Due to the growing availability of unstructured reviews, the proposed system gives a summarized form of the information that is obtained from the reviews in order to furnish customers with pin point or crisp results. DOI: 10.17762/ijritcc2321-8169.16049

Интернет и социальные медиа сегодня

В статье рассматриваются вопросы влияния Интернета на глобальную экономику и современный маркетинг, значение социальных медиа как новой среды общения людей, их роль в профессиональном образовании и ведении лесного хозяйства на Западе, а также – возможности социальных медиа в восстановлении разрушенной системы лесного хозяйства Росси

Feature-Based Opinion Classification Using the KPCA Technique: Concept and Performance Evaluation

Over the last several years, a widespread trend on the internet has been the proliferation of online evaluations written by people with whom they share their ideas, interests, experiences, and opinions. Opinion mining, also known as sentiment analysis, is the process of classifying pieces of text written in a natural language on a subject into positive, negative, or neutral categories according to the human emotions, views, and feelings that are communicated in that text. The field of sentiment analysis has progressed to the point that it can now analyse internet evaluations and provide significant information to people as well as corporations, which may assist these parties in the decision-making process. In the proposed model, feature extraction extracts the collection of features that are both semantically and statistically significant using the kernel principal component analysis (KPCA) method. According to the findings of the simulations, the suggested model performs better than other existing models

A model for the Twitter sentiment curve

Twitter is among the most used online platforms for the political communications, due to the concision of its messages (which is particularly suitable for political slogans) and the quick diffusion of messages. Especially when the argument stimulate the emotionality of users, the content on Twitter is shared with extreme speed and thus studying the tweet sentiment if of utmost importance to predict the evolution of the discussions and the register of the relative narratives. In this article, we present a model able to reproduce the dynamics of the sentiments of tweets related to specific topics and periods and to provide a prediction of the sentiment of the future posts based on the observed past. The model is a recent variant of the P\'olya urn, introduced and studied in arXiv:1906.10951 and arXiv:2010.06373, which is characterized by a "local" reinforcement, i.e. a reinforcement mechanism mainly based on the most recent observations, and by a random persistent fluctuation of the predictive mean. In particular, this latter feature is capable of capturing the trend fluctuations in the sentiment curve. While the proposed model is extremely general and may be also employed in other contexts, it has been tested on several Twitter data sets and demonstrated greater performances compared to the standard P\'olya urn model. Moreover, the different performances on different data sets highlight different emotional sensitivities respect to a public event.Comment: 19 pages, 12 figure

PENERAPAN ADABOOST UNTUK MENINGKATKAN AKURASI NAIVE BAYES PADA PREDIKSI PENDAPATAN PENJUALAN FILM

For economists and financial experts predicting the success of doing business is very interesting. With the data analytics the prediction process has been facilitated by the past data stored to find out what will happen in the future. This research was conducted to facilitate the film industry players in considering the factors that can influence the income of the film to be produced. The naive bayes method is a popular machine learning technique for classification because it is very simple, efficient, and has good performance on many domains. But naive bayes has a disadvantage that is very sensitive to too many features, thus making the accuracy to be low, in this case the adaboost method to reduce bias so that it can and improve accuracy from naive bayes. Validation is done by using 10 fold cross validation while measuring accuracy using confusion matrix and kappa. The results showed an increase in the accuracy of Naive Bayes from 83.22% to 84.44% and the kappa value from 0.706 to 0.731. So that it can be concluded that the application of adaboost on 2014 & 2015 CSM film data is able to improve the accuracy of the Naive Bayes algorith

CFLCA: High Performance based Heart disease Prediction System using Fuzzy Learning with Neural Networks

Human Diseases are increasing rapidly in today’s generation mainly due to the life style of people like poor diet, lack of exercises, drugs and alcohol consumption etc. But the most spreading disease that is commonly around 80% of people death direct and indirectly heart disease basis. In future (approximately after 10 years) maximum number of people may expire cause of heart diseases. Due to these reasons, many of researchers providing enormous remedy, data analysis in various proposed technologies for diagnosing heart diseases with plenty of medical data which is related to heart disease. In field of Medicine regularly receives very wide range of medical data in the form of text, image, audio, video, signal pockets, etc. This database contains raw dataset which consist of inconsistent and redundant data. The health care system is no doubt very rich in aspect of storing data but at the same time very poor in fetching knowledge. Data mining (DM) methods can help in extracting a valuable knowledge by applying DM terminologies like clustering, regression, segmentation, classification etc. After the collection of data when the dataset becomes larger and more complex than data mining algorithms and clustering algorithms (D-Tree, Neural Networks, K-means, etc.) are used. To get accuracy and precision values improved with proposed method of Cognitive Fuzzy Learning based Clustering Algorithm (CFLCA) method. CFLCA methodology creates advanced meta indexing for n-dimensional unstructured data. The heart disease dataset used after data enrichment and feature engineering with UCI machine learning algorithm, attain high level accurate and prediction rate. Through this proposed CFLCA algorithm is having high accuracy, precision and recall values of data analysis for heart diseases detection

Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of “likes (favorites)” and “retweets (RTs)” as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate